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Machine learning offers great potential for automated prediction of post-stroke symptoms and their response to rehabilitation. Major challenges for this endeavour include the very high dimensionality of neuroimaging data, the relatively small size of the datasets available for learning and interpreting the predictive features, as well as, how to effectively combine neuroimaging and tabular data (e.g. demographic information and clinical characteristics). This paper evaluates several solutions based on two strategies. The first is to use 2D images that summarise MRI scans. The second is to select key features that improve classification accuracy. Additionally, we introduce the novel approach of training a convolutional neural network (CNN) on images that combine regions-of-interests (ROIs) extracted from MRIs, with symbolic representations of tabular data. We evaluate a series of CNN architectures (both 2D and a 3D) that are trained on different representations of MRI and tabular data, to predict whether a composite measure of post-stroke spoken picture description ability is in the aphasic or non-aphasic range. MRI and tabular data were acquired from 758 English speaking stroke survivors who participated in the PLORAS study. Each participant was assigned to one of five different groups that were matched for initial severity of symptoms, recovery time, left lesion size and the months or years post-stroke that spoken description scores were collected. Training and validation were carried out on the first four groups. The fifth (lock-box/test set) group was used to test how well model accuracy generalises to new (unseen) data. The classification accuracy for a baseline logistic regression was 0.678 based on lesion size alone, rising to 0.757 and 0.813 when initial symptom severity and recovery time were successively added. The highest classification accuracy (0.854), area under the curve (0.899) and F1 score (0.901) were observed when 8 regions of interest were extracted from each MRI scan and combined with lesion size, initial severity and recovery time in a 2D Residual Neural Network (ResNet). This was also the best model when data were limited to the 286 participants with moderate or severe initial aphasia (with area under curve = 0.865), a group that would be considered more difficult to classify. Our findings demonstrate how imaging and tabular data can be combined to achieve high post-stroke classification accuracy, even when the dataset is small in machine learning terms. We conclude by proposing how the current models could be improved to achieve even higher levels of accuracy using images from hospital scanners., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Interoception, the representation of the body's internal state, serves as a foundation for emotion, motivation, and wellbeing. Yet despite its centrality in human experience, the neural mechanisms of interoceptive attention are poorly understood. The Interoceptive/Exteroceptive Attention Task (IEAT) is a novel neuroimaging paradigm that compares behavioral tracking of the respiratory cycle (Active Interoception) to tracking of a visual stimulus (Active Exteroception). Twenty-two healthy participants completed the IEAT during two separate scanning sessions ( N  = 44) as part of a randomized control trial of mindful awareness in body-oriented therapy (MABT). Compared with Active Exteroception, Active Interoception deactivated somatomotor and prefrontal regions. Greater self-reported interoceptive sensibility (MAIA scale) predicted sparing from deactivation within the anterior cingulate cortex (ACC) and left-lateralized language regions. The right insula, typically described as a primary interoceptive cortex, was only specifically implicated by its deactivation during an exogenously paced respiration condition (Active Matching) relative to self-paced Active Interoception. Psychophysiological interaction (PPI) analysis characterized Active Interoception as promoting greater ACC connectivity with lateral prefrontal and parietal regions commonly referred to as the dorsal attention network (DAN). In contrast to evidence relating accurate detection of liminal interoceptive signals such as the heartbeat to anterior insula activity, interoceptive attention toward salient signals such as the respiratory cycle may involve reduced cortical activity but greater ACC-DAN connectivity, with greater sensibility linked to reduced deactivation within the ACC and language-processing regions., Competing Interests: The authors declare no competing financial interests., (Copyright © 2023 Farb et al.)

The extent to which the neural systems underlying semantic processes degrade with advanced age remains unresolved, which motivated the current study of neural activation on functional magnetic resonance imaging (fMRI) during semantic judgments of associated vs. unassociated, semantic vs. rhyme, and abstract vs. rhyme word pairs. Thirty-eight older adults, 55-85 years of age, performed semantic association decision tasks in a mixed event-related block fMRI paradigm involving binary judgments as to whether word pairs were related (i.e., semantically associated). As hypothesized, significantly greater activation was evident during processing of associated (vs. unassociated) word pairs in cortical areas implicated in semantic processing, including the angular gyrus, temporal cortex, and inferior frontal cortex. Cortical areas showed greater activation to unassociated (vs. associated) word pairs, primarily within a large occipital cluster. Greater activation was evident in cortical areas when response to semantic vs. phonemic word pairs. Contrasting activation during abstract vs. concrete semantic processing revealed areas of co-activation to both semantic classes, and areas that had greater response to either abstract or concrete word pairs. Neural activation across conditions did not vary as a function of greater age, indicating only minimal age-associated perturbation in neural activation during semantic processing. Therefore, the response of the semantic hubs, semantic control, and secondary association areas appear to be largely preserved with advanced age among older adults exhibiting successful cognitive aging. These findings may provide a useful clinical contrast if compared to activation among adults experiencing cognitive decline due Alzheimer's, frontal-temporal dementia, and other neurodegenerative diseases., (© 2024. The Author(s), under exclusive licence to American Aging Association.)

Controversy surrounds the interpretation of higher activation for pseudoword compared to word reading in the left precentral gyrus and pars opercularis. Specifically, does activation in these regions reflect: (1) the demands on sublexical assembly of articulatory codes, or (2) retrieval effort because the combinations of articulatory codes are unfamiliar? Using fMRI, in 84 neurologically intact participants, we addressed this issue by comparing reading and repetition of words (W) and pseudowords (P) to naming objects (O) from pictures or sounds. As objects do not provide sublexical articulatory cues, we hypothesis that retrieval effort will be greater for object naming than word repetition/reading (which benefits from both lexical and sublexical cues); while the demands on sublexical assembly will be higher for pseudoword production than object naming. We found that activation was: (i) highest for pseudoword reading [P>O&W in the visual modality] in the anterior part of the ventral precentral gyrus bordering the precentral sulcus (vPCg/vPCs), consistent with the sublexical assembly of articulatory codes; but (ii) as high for object naming as pseudoword production [P&O>W] in dorsal precentral gyrus (dPCg) and the left inferior frontal junction (IFJ), consistent with retrieval demands and cognitive control. In addition, we dissociate the response properties of vPCg/vPCs, dPCg and IFJ from other left frontal lobe regions that are activated during single word speech production. Specifically, in both auditory and visual modalities: a central part of vPCg (head and face area) was more activated for verbal than nonverbal stimuli [P&W>O]; and the pars orbitalis and inferior frontal sulcus were most activated during object naming [O>W&P]. Our findings help to resolve a previous discrepancy in the literature, dissociate three functionally distinct parts of the precentral gyrus, and refine our knowledge of the functional anatomy of speech production in the left frontal lobe., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Working memory (WM), a core component of executive functions, relies on a dedicated brain system that maintains and stores information in the short term. While extensive neuroimaging research has identified a distributed set of neural substrates relevant to WM, their underlying molecular mechanisms remain enigmatic. This study investigated the neural correlates of WM as well as their underlying molecular mechanisms., Results: Our voxel-wise analyses of resting-state functional MRI data from 502 healthy young adults showed that better WM performance (higher accuracy and shorter reaction time of the 3-back task) was associated with lower functional connectivity density (FCD) in the left inferior temporal gyrus and higher FCD in the left anterior cingulate cortex. A combination of transcriptome-neuroimaging spatial correlation and the ensemble-based gene category enrichment analysis revealed that the identified neural correlates of WM were associated with expression of diverse gene categories involving important cortical components and their biological processes as well as sodium channels. Cross-region spatial correlation analyses demonstrated significant associations between the neural correlates of WM and a range of neurotransmitters including dopamine, glutamate, serotonin, and acetylcholine., Conclusions: These findings may help to shed light on the molecular mechanisms underlying the neural correlates of WM., (© 2024. The Author(s).)

Different kinds of traumatic experiences like natural catastrophes vs. relational traumatic experiences (e.g., sex/physical abuse, interpersonal partner violence) are involved in the development of the self and PTSD psychopathological manifestations. Looking at a neuroscience approach, it has been proposed a nested hierarchical model of self, which identifies three neural-mental networks: (i) interoceptive; (ii) exteroceptive; (iii) mental. However, it is still unclear how the self and its related brain networks might be affected by non-relational vs relational traumatic experiences. Departing from this background, the current study aims at conducting a meta-analytic review of task-dependent fMRI studies (i.e., emotional processing task) among patients with PTSD due to non-relational (PTSD-NR) and relational (PTSD-R) traumatic experiences using two approaches: (i) a Bayesian network meta-analysis for a region-of-interest-based approach; (ii) a coordinated-based meta-analysis. Our findings suggested that the PTSD-NR mainly recruited areas ascribed to the mental self to process emotional stimuli. Whereas, the PTSD-R mainly activated regions associated with the intero-exteroceptive self. Accordingly, the PTSD-R compared to the PTSD-NR might not reach a higher symbolic capacity to process stimuli with an emotional valence. These results are also clinically relevant in support of the development of differential treatment approaches for non-relational vs. relational PTSD., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

Anticipating the behaviour of moving objects in the physical environment is essential for a wide range of daily actions. This ability is thought to rely on mental simulations and has been shown to involve frontoparietal and early visual areas. Yet, the connectivity patterns between these regions during intuitive physical inference remain largely unknown. In this study, participants underwent fMRI while performing a task requiring them to infer the parabolic trajectory of an occluded ball falling under Newtonian physics, and a control task. Building on our previous research showing that when solving the physical inference task, early visual areas encode task-specific and perception-like information about the inferred trajectory, the present study aimed to (i) identify regions that are functionally coupled with early visual areas during the physical inference task, and (ii) investigate changes in effective connectivity within this network of regions. We found that early visual areas are functionally connected to a set of parietal and premotor regions when inferring occluded trajectories. Using dynamic causal modelling, we show that predicting occluded trajectories is associated with changes in effective connectivity within a parieto-premotor network, which may drive internally generated early visual activity in a top-down fashion. These findings offer new insights into the interaction between early visual and frontoparietal regions during physical inference, contributing to our understanding of the neural mechanisms underlying the ability to predict physical outcomes., (© 2024. The Author(s).)

Objectives: Reliable detection of disease-specific atrophy in individual T1w-MRI by voxel-based morphometry (VBM) requires scanner-specific normal databases (NDB), which often are not available. The aim of this retrospective study was to design, train, and test a deep convolutional neural network (CNN) for single-subject VBM without the need for a NDB (CNN-VBM)., Materials and Methods: The training dataset comprised 8945 T1w scans from 65 different scanners. The gold standard VBM maps were obtained by conventional VBM with a scanner-specific NDB for each of the 65 scanners. CNN-VBM was tested in an independent dataset comprising healthy controls (n = 37) and subjects with Alzheimer's disease (AD, n = 51) or frontotemporal lobar degeneration (FTLD, n = 30). A scanner-specific NDB for the generation of the gold standard VBM maps was available also for the test set. The technical performance of CNN-VBM was characterized by the Dice coefficient of CNN-VBM maps relative to VBM maps from scanner-specific VBM. For clinical testing, VBM maps were categorized visually according to the clinical diagnoses in the test set by two independent readers, separately for both VBM methods., Results: The VBM maps from CNN-VBM were similar to the scanner-specific VBM maps (median Dice coefficient 0.85, interquartile range [0.81, 0.90]). Overall accuracy of the visual categorization of the VBM maps for the detection of AD or FTLD was 89.8% for CNN-VBM and 89.0% for scanner-specific VBM., Conclusion: CNN-VBM without NDB provides a similar performance in the detection of AD- and FTLD-specific atrophy as conventional VBM., Clinical Relevance Statement: A deep convolutional neural network for voxel-based morphometry eliminates the need of scanner-specific normal databases without relevant performance loss and, therefore, could pave the way for the widespread clinical use of voxel-based morphometry to support the diagnosis of neurodegenerative diseases., Key Points: • The need of normal databases is a barrier for widespread use of voxel-based brain morphometry. • A convolutional neural network achieved a similar performance for detection of atrophy than conventional voxel-based morphometry. • Convolutional neural networks can pave the way for widespread clinical use of voxel-based morphometry., (© 2023. The Author(s).)

Background: In neuroscience, accurately quantifying individual brain regions in large cohorts is a challenge. Differences in intracranial structures can suggest functional differences, but they also reflect the effects of other factors. However, there is currently no standardized method for the correction of intracranial structure measurements., Purpose: To identify the optimal method to counteract the influence of total intracranial volume (TIV) and gender on the measurement of intracranial structures., Study Type: Prospective., Population/subjects: One hundred forty-one healthy adult volunteers (70 male, mean age 21.8 ± 1.7 years)., Field Strength/sequence: T1-weighted 3D gradient-echo sequence at 3.0 T., Assessment: A radiologist with 5 years of work experience screened the raw images to exclude poor-quality images. Freesurfer then performed automated segmentation to obtain measurements of intracranial structures. Male-only, female-only, and TIV-matched sub-samples were created separately. Comparisons between the original data and these sub-samples were used to assess the effects of gender and TIV. Comparison the consistency between TIV-matched sample and corrected data that corrected by four methods: Proportion method, power-corrected proportion method, covariate regression method, and residual method., Statistical Tests: Cohen's d for examining group distribution disparities, t-tests for probing mean differences, correlation coefficients to assess the relationships between intracranial substructure measurements and TIV. Multiple comparison corrections were applied to the results., Results: The correlation coefficients between TIV and the volumes of intracranial structures ranged from 0.033 to 0.883, with an average of 0.467. Thirty significant volume differences were found among 36 structures in the original sample, while no differences were observed in the TIV-matched sample. Among the four correction methods, the residual method had highest consistency (similarity 94.4%) with the TIV-matched group., Data Conclusion: The variation in intracranial structure sizes between genders was largely attributable to TIV. The residual method offers a more accurate and effective approach for correcting the effects of TIV on intracranial structures., Evidence Level: 2 TECHNICAL EFFICACY: Stage 1., (© 2023 International Society for Magnetic Resonance in Medicine.)

Dynamic Causal Modelling (DCM) is the predominant method for inferring effective connectivity from neuroimaging data. In the 15 years since its introduction, the neural models and statistical routines in DCM have developed in parallel, driven by the needs of researchers in cognitive and clinical neuroscience. In this guide, we step through an exemplar fMRI analysis in detail, reviewing the current implementation of DCM and demonstrating recent developments in group-level connectivity analysis. In the appendices, we detail the theory underlying DCM and the assumptions (i.e., priors) in the models. In the first part of the guide (current paper), we focus on issues specific to DCM for fMRI. This is accompanied by all the necessary data and instructions to reproduce the analyses using the SPM software. In the second part (in a companion paper), we move from subject-level to group-level modelling using the Parametric Empirical Bayes framework, and illustrate how to test for commonalities and differences in effective connectivity across subjects, based on imaging data from any modality., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

See Thompson and Woollams (doi:10.1093/brain/awx264) for a scientific commentary on this article. Previous research with aphasic patients has shown that picture naming can be facilitated by concurrent phonemic cueing [e.g. initial phoneme(s) of the word that the patient is trying to retrieve], both as an immediate word retrieval technique, and when practiced repeatedly over time as a long-term anomia treatment. Here, to investigate the neural mechanisms supporting word retrieval, we adopted—for the first time—a functional magnetic resonance imaging task using the same naming procedure as it occurs during the anomia treatment process. Before and directly after a 6-week anomia treatment programme, 18 chronic aphasic stroke patients completed our functional magnetic resonance imaging protocol—a picture naming task aided by three different types of phonemic cues (whole words, initial phonemes, final phonemes) and a noise-control condition. Patients completed a naming task based on the training materials, and a more general comprehensive battery of language tests both before and after the anomia treatment, to determine the effectiveness and specificity of the therapy. Our results demonstrate that the anomia treatment was effective and specific to speech production, significantly improving both patients’ naming accuracy and reaction time immediately post-treatment (unstandardized effect size: 29% and 17%, respectively; Cohen’s d: 3.45 and 1.83). Longer term gains in naming were maintained 3 months later. Functional imaging results showed that both immediate and long-term facilitation of naming involved a largely overlapping bilateral frontal network including the right anterior insula, inferior frontal and dorsal anterior cingulate cortices, and the left premotor cortex. These areas were associated with a neural priming effect (i.e. reduced blood oxygen level-dependent signal) during both immediate (phonemically-cued versus control-cue conditions), and long-term facilitation of naming (i.e. treated versus untreated items). Of note is that different brain regions were sensitive to different phonemic cue types. Processing of whole word cues was associated with increased activity in the right angular gyrus; whereas partial word cues (initial and final phonemes) recruited the left supplementary motor area, and right anterior insula, inferior frontal cortex, and basal ganglia. The recruitment of multiple and bilateral areas may help explain why phonemic cueing is such a successful behavioural facilitation tool for anomia treatment. Our results have important implications for optimizing current anomia treatment approaches, developing new treatments, and improving speech outcome for aphasic patients.

Transcranial magnetic stimulation focused on either the left anterior supramarginal gyrus or opercular part of the left inferior frontal gyrus has been reported to transiently impair the ability to perform phonological more than semantic tasks. Here we tested whether phonological processing abilities were also impaired following lesions to these regions in right-handed, English speaking adults, who were investigated at least 1 year after a left-hemisphere stroke. When our regions of interest were limited to 0.5 cm3 of grey matter centred around sites that had been identified with transcranial magnetic stimulation-based functional localization, phonological impairments were observed in 74% (40/54) of patients with damage to the regions and 21% (21/100) of patients sparing these regions. This classification accuracy was better than that observed when using regions of interest centred on activation sites in previous functional magnetic resonance imaging studies of phonological processing, or transcranial magnetic stimulation sites that did not use functional localization. New regions of interest were generated by redefining the borders of each of the transcranial magnetic stimulation sites to include areas that were consistently damaged in the patients with phonological impairments. This increased the incidence of phonological impairments in the presence of damage to 85% (46/54) and also reduced the incidence of phonological impairments in the absence of damage to 15% (15/100). The difference in phonological processing abilities between those with and without damage to these 'transcranial magnetic stimulation-guided' regions remained highly significant even after controlling for the effect of lesion size. The classification accuracy of the transcranial magnetic stimulation-guided regions was validated in a second sample of 108 patients and found to be better than that for (i) functional magnetic resonance imaging-guided regions; (ii) a region identified from an unguided lesion overlap map; and (iii) a region identified from voxel-based lesion-symptom mapping. Finally, consistent with prior findings from functional imaging and transcranial magnetic stimulation in healthy participants, we show how damage to our transcranial magnetic stimulation-guided regions affected performance on phonologically more than semantically demanding tasks. The observation that phonological processing abilities were impaired years after the stroke, suggests that other brain regions were not able to fully compensate for the contribution that the transcranial magnetic stimulation-guided regions make to language tasks. More generally, our novel transcranial magnetic stimulation-guided lesion-deficit mapping approach shows how non-invasive stimulation of the healthy brain can be used to guide the identification of regions where brain damage is likely to cause persistent behavioural effects., (© The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain.)

Purpose: In patients with drug-resistant focal epilepsy, surgical resection is often the only treatment option to achieve long-term seizure control. Prior to brain surgery involving potential language areas, identification of hemispheric language dominance is crucial. Our group developed and validated a functional magnetic resonance imaging (fMRI) battery of four pediatric language tasks. The present study aimed at optimizing fMRI data acquisition and analysis using these tasks., Methods: We retrospectively analyzed speech fMRI examinations of 114 neuropediatric patients (age range 5.8-17.8 years) who were examined prior to possible epilepsy surgery. In order to evaluate hemispheric language dominance, 1-4 language tasks (vowel identification task VIT, word-chain task WCT, beep-story task BST, synonym task SYT) were measured., Results: Language dominance was classified using fMRI activation in the 13 validly lateralizing ROIs (VLR) in frontal, temporal and parietal lobes and cerebellum of the recent validation study from our group: 47/114 patients were classified as left-dominant, 34/114 as bilateral and 6/114 as right-dominant. In an attempt to enlarge the set of VLR, we then compared for each task agreement of these ROI activations with the classified language dominance. We found four additional task-specific ROIs showing concordant activation and activation in ≥ 10 sessions, which we termed validly lateralizing (VLR new ). The new VLRs were: for VIT the temporal language area and for SYT the middle frontal gyrus, the intraparietal sulcus and cerebellum. Finally, in order to find the optimal sequence of measuring the different tasks, we analyzed the success rates of single tasks and all possible task combinations. The sequence 1) VIT 2) WCT 3) BST 4) SYT was identified as the optimal sequence, yielding the highest chance to obtain reliable results even when the fMRI examination has to be stopped, e.g., due to lack of cooperation., Conclusion: Our suggested task order together with the enlarged set of VLR new may contribute to optimize pediatric speech fMRI in a clinical setting., (© 2021. The Author(s).)

Deep learning algorithms have greatly improved our ability to estimate eloquent cortex regions from resting-state brain scans for patients about to undergo neurosurgery. The use of deep learning has the potential to fully automate functional mapping of cortex in this context. We present a highly focused state-of-the-art review on current technology for estimating eloquent cortex from resting-state functional magnetic resonance scans and identify potential paths to meet this goal in the future., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Objectives: Lateralisation of some language pathways has been reported in the literature using diffusion tractography, which is more feasible than functional magnetic resonance imaging (fMRI) in challenging patients. Our retrospective study investigates whether a correlation exists between threshold-independent fMRI language lateralisation and structural lateralisation using tractography in healthy controls and brain tumour patients., Methods: Fifteen healthy subjects and 61 patients underwent language fMRI and diffusion-weighted MRI. A regional fMRI laterality index (LI) was calculated. Tracts dissected were the arcuate fasciculus (long direct and short indirect tracts), uncinate fasciculus, inferior longitudinal fasciculus, inferior fronto-occipital fasciculus and frontal aslant tract. An asymmetry index (AI) for each tract was calculated using tract volume analysed with single tensor (ST) and spherical deconvolution (SD) models, as well as hindrance modulated orientational anisotropy (HMOA) for SD tracts. Linear regression assessed the correlation between LI and AI., Results: In all subjects, there was no significant correlation between LI and AI for any of the dissected tracts. Significant correlations were only found when handedness for controls and tumour volume for patients were included as covariates. In handedness subgroups, the average AI of some tracts showed the same laterality as LI, and some the opposite. Discordant results were observed for ST- and SD-based AIs., Conclusions: Our results do not support using tractography in the assessment of language lateralisation. The discordant results between ST and SD indicate that either the structural lateralisation of dissected tracts is less robust than functional lateralisation, or tractography is not sensitive methodology. Other diffusion analysis approaches should be developed., Clinical Relevance Statement: Although diffusion tractography may be more feasible than fMRI in challenging tumour patients and where sedation or anaesthesia is required, our results do not currently recommend replacing fMRI with tractography using volume or HMOA in the assessment of language lateralisation., Key Points: • No correlation found between fMRI and tractography in language lateralisation. • Discordance between asymmetry indices of different tractography models and metrics. • Tractography not currently recommended in language lateralisation assessment., (© 2023. The Author(s), under exclusive licence to European Society of Radiology.)

Functional neuroimaging studies are revealing the neural systems sustaining many sensory, motor and cognitive abilities. A proper understanding of these systems requires an appreciation of the degree to which they vary across subjects. Some sources of inter-subject variability might be easy to measure (demographics, behavioural scores, or experimental factors), while others are more difficult (cognitive strategies, learning effects, and other hidden sources). Here, we introduce a simple way of visualising whole-brain consistency and variability in brain responses across subjects using threshold-weighted voxel-based overlap maps. The output quantifies the proportion of subjects activating a particular voxel or region over a wide range of statistical thresholds. The sensitivity of our approach was assessed in 30 healthy adults performing a matching task with their dominant hand. We show how overlap maps revealed many effects that were only present in a subsample of our group; we discuss how overlap maps can provide information that may be missed or misrepresented by standard group analysis, and how this information can help users to understand their data. In particular, we emphasize that functional overlap maps can be particularly useful when it comes to explaining typical (or atypical) compensatory mechanisms used by patients following brain damage.

Purpose: To validate four established, child-friendly functional magnetic resonance imaging (fMRI) language tasks (word chain task [WCT], vowel identification task [VIT], synonym task [SYT] and beep story task [BST]) in a predominantly pediatric cohort., Methods: Intracarotid amobarbital procedures (IAP) (n = 17) and unchanged language after hemispherotomy (n = 6) were used as gold standards. The fMRI activations of nine regions of interest (ROI) in the frontal, temporal and parietal lobes as well as in the cerebellum were visually assessed in 23 fMRI examinations (in total 117 fMRI task sessions) of 23 patients (age range 10.0-23.0 years) with drug-refractory epilepsies., Results: The ROIs were considered valid when they showed activation in more than 25% of all sessions for the respective task and never showed false lateralization (in comparison to gold standards). Thus, 13 valid, task-specific ROIs were identified: 5 ROIs for the WCT (frontal operculum, inferior frontal gyrus, middle frontal gyrus, intraparietal sulcus, cerebellum), 3 ROIs for the VIT (frontal operculum, inferior frontal gyrus, middle frontal gyrus), 3 ROIs for the SYT (frontal operculum, inferior frontal gyrus, temporal language area) and 2 ROIs for the BST (inferior frontal gyrus, middle frontal gyrus)., Conclusion: Clinical fMRI using the battery of four tasks is a valid tool for lateralizing language in children, adolescents and young adults. Each task proved to be specifically useful, which confirms that applying different tasks increases the probability of diagnosing language dominance in presurgical candidates.

Preadolescence is a critical period characterized by dramatic morphological changes and accelerated cortico-subcortical development. Moreover, the coordinated development of cortical and subcortical regions underlies the emerging cognitive functions during this period. Deviations in this maturational coordination may underlie various psychiatric disorders that begin during preadolescence, but to date these deviations remain largely uncharted. We constructed a comprehensive whole-brain morphometric similarity network (MSN) from 17 neuroimaging modalities in a large preadolescence sample (N = 8908) from Adolescent Brain Cognitive Development (ABCD) study and investigated its association with 10 cognitive subscales and 27 psychiatric subscales or diagnoses. Based on the MSNs, each brain was clustered into five modules with distinct cytoarchitecture and evolutionary relevance. While morphometric correlation was positive within modules, it was negative between modules, especially between isocortical and paralimbic/subcortical modules; this developmental dissimilarity was genetically linked to synapse and neurogenesis. The cortico-subcortical dissimilarity becomes more pronounced longitudinally in healthy children, reflecting developmental differentiation of segregated cytoarchitectonic areas. Higher cortico-subcortical dissimilarity (between the isocortical and paralimbic/subcortical modules) were related to better cognitive performance. In comparison, children with poor modular differentiation between cortex and subcortex displayed higher burden of externalizing and internalizing symptoms. These results highlighted cortical-subcortical morphometric dissimilarity as a dynamic maturational marker of cognitive and psychiatric status during the preadolescent stage and provided insights into brain development., (© 2022. The Author(s), under exclusive licence to Springer Nature Limited.)

Background: Studying brain interindividual variations has recently gained interest to understand different human behaviors. It is particularly important to investigate how a variety of functional differences can be associated with a few differences in brain structure. It would be more meaningful if such an investigation is performed jointly at the network level to connect structural building blocks to functional variations modules., Purpose: To decompose the interindividual variations of brain in the form of mutual functional and structural subnetworks based on a data-driven approach., Study Type: Retrospective., Population: In all, 92 healthy subjects., Field Strength/sequence: 3T Siemens/MPRAGE, diffusion spectrum imaging (DSI) acquisition protocol, gradient echo sequence., Assessment: The proposed approach was quantitatively assessed by examining the consistency of the networks against the number of subjects. Distribution of the obtained components across brain regions was studied and their relevance was qualitatively evaluated by comparison to variations that had been independently reported previously., Statistical Tests: Permutation test, two-sample t-test, Pearson correlation coefficient., Results: Ten pairs of components including functional and structural subnetworks were obtained. Assessing the reproducibility of the proposed method with respect to the sample size indicated reliable detection of connections (above 70%) for all components by reducing the number of subjects to 70. Specifically, one of the functional subnetworks can be used to distinguish left-handed from right-handed people (P = 2.6 × 10 -8 ) as the basic interindividual variation. This functional subnetwork has a main overlap (40.18%) with the somatomotor system and the Broca part was captured in its corresponding structural subnetwork., Data Conclusion: These results show that the proposed method can reveal intersubject variations systematically through a mathematical algorithm of joint independent component analysis. They confirm that intersubject variations can be expressed in the form of building blocks. In contrast to the functional subnetworks that were discoverable independently, their structural counterparts were found and interpreted only in conjunction with the functional subnetworks., Level of Evidence: 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2020;51:1779-1788., (© 2019 International Society for Magnetic Resonance in Medicine.)

The anatomy of language has been investigated with PET or fMRI for more than 20 years. Here I attempt to provide an overview of the brain areas associated with heard speech, speech production and reading. The conclusions of many hundreds of studies were considered, grouped according to the type of processing, and reported in the order that they were published. Many findings have been replicated time and time again leading to some consistent and undisputable conclusions. These are summarised in an anatomical model that indicates the location of the language areas and the most consistent functions that have been assigned to them. The implications for cognitive models of language processing are also considered. In particular, a distinction can be made between processes that are localized to specific structures (e.g. sensory and motor processing) and processes where specialisation arises in the distributed pattern of activation over many different areas that each participate in multiple functions. For example, phonological processing of heard speech is supported by the functional integration of auditory processing and articulation; and orthographic processing is supported by the functional integration of visual processing, articulation and semantics. Future studies will undoubtedly be able to improve the spatial precision with which functional regions can be dissociated but the greatest challenge will be to understand how different brain regions interact with one another in their attempts to comprehend and produce language., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Background: In medical practice, diagnostic hypotheses are often made by physicians in the first moments of contact with patients; sometimes even before they report their symptoms. We propose that generation of diagnostic hypotheses in this context is the result of cognitive processes subserved by brain mechanisms that are similar to those involved in naming objects or concepts in everyday life., Methodology and Principal Findings: To test this proposal we developed an experimental paradigm with functional magnetic resonance imaging (fMRI) using radiological diagnosis as a model. Twenty-five radiologists diagnosed lesions in chest X-ray images and named non-medical targets (animals) embedded in chest X-ray images while being scanned in a fMRI session. Images were presented for 1.5 seconds; response times (RTs) and the ensuing cortical activations were assessed. The mean response time for diagnosing lesions was 1.33 (SD ±0.14) seconds and 1.23 (SD ±0.13) seconds for naming animals. 72% of the radiologists reported cogitating differential diagnoses during trials (3.5 seconds). The overall pattern of cortical activations was remarkably similar for both types of targets. However, within the neural systems shared by both stimuli, activation was significantly greater in left inferior frontal sulcus and posterior cingulate cortex for lesions relative to animals., Conclusions: Generation of diagnostic hypotheses and differential diagnoses made through the immediate visual recognition of clinical signs can be a fast and automatic process. The co-localization of significant brain activation for lesions and animals suggests that generating diagnostic hypotheses for lesions and naming animals are served by the same neuronal systems. Nevertheless, diagnosing lesions was cognitively more demanding and associated with more activation in higher order cortical areas. These results support the hypothesis that medical diagnoses based on prompt visual recognition of clinical signs and naming in everyday life are supported by similar brain systems.

A number of modern digital anatomy techniques, based on structural MR brain images, have recently become applicable to the non-human primate brain. Such voxel-based quantitative techniques require a species-specific standardized brain template. Here we present a brain template for the Japanese macaque (Macaca fuscata). The template was designed to be used as a tool for spatially normalising Japanese macaque brains into a standard space. Although this species of macaque monkey is widely used in neuroscience research, including studies of higher cognitive brain functions, no standard MRI template of its brain is presently available. The template presented here is based on T1/T2* weighted, high-resolution 4T MR images obtained from 16 male adult Japanese macaque monkeys. T1/T2* images were used to correct the signal inequalities resulting from the use of a surface coil. Based on these images, population-averaged probability maps were created for grey matter, white matter and cerebrospinal fluid. The new template presented here should facilitate future brain research using the Japanese macaque monkey. Whole brain templates are available at http://brainatlas.brain.riken.jp/jm/modules/xoonips/listitem.php?index_id=9., (Copyright 2010 Elsevier Inc. All rights reserved.)

In this review of 100 fMRI studies of speech comprehension and production, published in 2009, activation is reported for: prelexical speech perception in bilateral superior temporal gyri; meaningful speech in middle and inferior temporal cortex; semantic retrieval in the left angular gyrus and pars orbitalis; and sentence comprehension in bilateral superior temporal sulci. For incomprehensible sentences, activation increases in four inferior frontal regions, posterior planum temporale, and ventral supramarginal gyrus. These effects are associated with the use of prior knowledge of semantic associations, word sequences, and articulation that predict the content of the sentence. Speech production activates the same set of regions as speech comprehension but in addition, activation is reported for: word retrieval in left middle frontal cortex; articulatory planning in the left anterior insula; the initiation and execution of speech in left putamen, pre-SMA, SMA, and motor cortex; and for suppressing unintended responses in the anterior cingulate and bilateral head of caudate nuclei. Anatomical and functional connectivity studies are now required to identify the processing pathways that integrate these areas to support language.

In this study we illustrate how the functional networks involved in a single task (e.g. the sensory, cognitive and motor components) can be segregated without cognitive subtractions at the second-level. The method used is based on meaningful variability in the patterns of activation between subjects with the assumption that regions belonging to the same network will have comparable variations from subject to subject. fMRI data were collected from thirty nine healthy volunteers who were asked to indicate with a button press if visually presented words were semantically related or not. Voxels were classified according to the similarity in their patterns of between-subject variance using a second-level unsupervised fuzzy clustering algorithm. The results were compared to those identified by cognitive subtractions of multiple conditions tested in the same set of subjects. This illustrated that the second-level clustering approach (on activation for a single task) was able to identify the functional networks observed using cognitive subtractions (e.g. those associated with vision, semantic associations or motor processing). In addition the fuzzy clustering approach revealed other networks that were not dissociated by the cognitive subtraction approach (e.g. those associated with high- and low-level visual processing and oculomotor movements). We discuss the potential applications of our method which include the identification of "hidden" or unpredicted networks as well as the identification of systems level signatures for different subgroupings of clinical and healthy populations.

In this functional magnetic resonance imaging study, we investigated human brain mechanisms that are involved in the analysis of voices as sound sources and in the pre-semantic analysis of voice information. The source of the voice was altered by changing the speaker, and the salience of the voice was altered by changing the amount of spectrotemporal detail. We identified a mechanism for detecting a change in the source of the voice in the posterior superior temporal lobe and anatomically distinct mechanisms for the detailed analysis of voice information in a bilateral network extending from the posterior to the anterior superior temporal lobe surrounding the superior temporal sulcus. The findings are consistent with a processing hierarchy in which general source attributes are analyzed in the posterior superior temporal lobe, abstraction of voice identity features occurs in posterior superior temporal sulcus, and further analysis of voice information occurs in anterior superior temporal sulcus and higher order cortices in the middle and anterior temporal lobe.

Functional neuroimaging can be used to characterize two types of abnormality in patients with neurological deficits: abnormal functional segregation and abnormal functional integration. In this paper we consider the factors that influence the experimental design, analysis, and interpretation of such studies. With respect to experimental design, we emphasize that: 1) task selection is constrained to tasks the patient is able to perform correctly, and 2) the most sensitive designs entail presenting stimuli of the same type close together. In terms of data preprocessing, prior to statistical analysis, we note that structural pathology may call for constraints on nonlinear transformations, used by spatial normalization, to prevent distortion of intact tissue. This means that one may have to increase spatial smoothing to reduce the impact of inaccurate normalization. Important issues in statistical modeling concern the first level of analysis (estimation of activation within subject), which has to distinguish correct from incorrect responses. At the second level (between subjects), inference should be based on between-subjects variance. Provided that these and other constraints are met, deficits in functional segregation are indicated when activation in one or a set of regions is higher or lower in patients relative to control subjects. In contrast, deficits in functional integration are implied when the influence of one brain region on another is stronger or weaker in patients relative to control subjects., (Copyright 2006 Wiley-Liss, Inc.)

We used functional Magnetic Resonance Imaging to explore the neural correlates of semantic relevance in 12 healthy participants performing a picture-naming task. In addition, we tested the hypothesis that category effects typically found in functional imaging can be partly explained in terms of different semantic relevance for animals and artefacts. We report that semantic relevance modulates neuronal responses in the medial fusiform gyrus bilaterally. As predicted, category effects in this region are strongly modulated by the semantic relevance of the items. Specifically, the effect of artefacts > animals is greatly reduced when the two categories are matched for semantic relevance. Thus, the present study demonstrates that neuronal responses during concept retrieval are modulated by the semantic relevance of the features. It also suggests that increased activation in the medial fusiform gyrus typically found for artefacts > animals can be explained by different semantic relevance for animal and artefact items.

Purpose of Review: Functional neuro-imaging studies of aphasic stroke offer the potential for a better understanding of the neuronal mechanisms that sustain language recovery. Conclusions, however, have been hampered by a set of unexpected challenges related to experimental design and interpretation. In this review of studies published between January 2004 and February 2005, we discuss imaging studies of speech production and comprehension in patients with aphasia after left hemisphere stroke., Recent Findings: Studies of speech production suggest that recovery depends on slowly evolving activation changes in the left hemisphere. In contrast, right hemisphere activation changes have been interpreted in terms of transcallosal disinhibition that do not reflect recovery because they occur early after stroke, in areas homologous to the lesion, and do not appear to correlate with the level of recovery. There have been few studies of auditory speech comprehension, but unlike speech production, recovery of speech comprehension appears to depend on both left and right temporal lobe activation., Summary: Together, recent studies provide a deeper appreciation of how the neuronal mechanisms of recovery depend on the task, the lesion site, the time from insult and the distinction between neuronal reorganization that does and does not sustain recovery. Although many more studies of aphasic stroke are required with larger patient numbers and more focal lesion sites, we also argue that clinical diagnosis and treatment requires a better understanding of the normal variability in functional anatomy and the many neuronal pathways that are available to sustain each type of language task.

In acute ischemic stroke, the diffusion-weighted imaging (DWI) lesion is widely held to represent the core of irreversible damage and is therefore crucial in selecting patients for thrombolysis. However, recent research suggests it may also represent penumbra. An illustrative patient was imaged 7 hours after stroke onset with back-to-back 3T diffusion tensor imaging and quantitative positron emission tomography, which showed a DWI lesion and misery perfusion, respectively. Using previously validated voxel-based probabilistic CBF, CMRO2, and Oxygen Extraction Fraction (OEF) thresholds, the authors show that the DWI lesion contained not only core but also substantial proportions of penumbra. Also, severe apparent diffusion coefficient reductions were present within the potentially salvageable penumbra as well as in the core. These findings have potential implications regarding treatment decisions.

In this study, we combined functional magnetic resonance imaging (fMRI) and dynamic causal modeling (DCM) to investigate whether object category effects in the occipital anti temporal cortex are mediated by inputs from early visual cortex or parietal regions. Resolving this issue may provide anatomical constraints on theories of category specificity--which make different assumptions about the underlying neurophysiology. The data were acquired by Ishai, Ungerleider, Martin, Schouten, and Haxby (1999, 2000) and provided by the National fiNRI Data Center (http://www.fmridcc.org). The original authors used a conventional analysis to estimate differential effects in the occipital anti temporal cortex in response to pictures of chairs, faces, and houses. We extended this approach by estimating neuronal interactions that mediate category effects using DCM. DCM uses a Bayesian framework to estimate and make inferences about the influence that one region exerts over another and how this is affected by experimental changes. DCM differs from previous approaches to brain connectivity, such as multivariate autoregressive models and structural equation modeling, as it assumes that the observed hemodynamic responses are driven by experimental changes rather than endogenous noise. DCM therefore brings the analysis of brain connectivity much closer to the analysis of regionally specific effects usually applied to functional imaging data. We used DCM to estimate the influence that V3 and the superior/inferior parietal cortex exerted over category-responsive regions and how this was affected by the presentation of houses, faces, and chairs. We found that category effects in occipital and temporal cortex were mediated by inputs from early visual cortex. In contrast,the connectivity from the superior/inferior parietal area to the category-responsive areas was unaffected by the presentation of chairs, faces, or houses. These findings indicate that category effects in the occipital and temporal cortex can be mediated by bottom-up mechanisms-a finding that needs to be embraced by models of category specificity.

In this study we demonstrate that, even in blocked design fMRI, an event-related analysis may provide a more accurate model of the hemodynamic responses than an epoch-related analysis. This is because the temporal shape of the predicted response differs between the event-related and the epoch model, with the former reaching its peak sooner and returning to baseline later than the latter. We present data from a blocked design fMRI study of single word reading alternated with rest. Conventionally, such a design would be analyzed using an epoch analysis with boxcar regressors. However, here we used a combined model in which trials were modeled as both single events and epochs. This allowed us to estimate the variance in the BOLD signal that was explained by either the event-related or the epoch regressors having discounted the effect of the other. We found that, in a number of language regions, the event-related model explained changes in activity that were not accounted for by the epoch model. In addition, we show that the advantage of the event-related over epoch model was engendered by its early onset rather than its late offset, relative to the epoch model.

This technical note deals with a priori estimation of efficiency of functional magnetic resonance imaging (fMRI) designs. The efficiency of an estimator is a measure of how reliable it is and depends on error variance (the variance not modeled by explanatory variables in the design matrix) and the design variance (a function of the explanatory variables and the contrast tested). Changes in the experimental design can induce changes in the variance of estimated responses. This translates into changes in the standard error of the response estimate or equivalently into changes in efficiency. One consequence is that statistics, testing for the same activation in different contexts (i.e., experimental designs), can change substantially even if the activation and error variance are exactly the same. We demonstrate this effect using an event-related fMRI study of single word reading during blocked and randomized trial presentations. Furthermore, we show that the error variance can change with the experimental design. This highlights a problem with a priori comparison of efficiency for two or more experimental designs, which usually assumes identical error variance.

We combined complementary non-invasive brain imaging techniques with behavioural measures to investigate the anatomy and time course of brain activity associated with semantic priming in a lexical-decision task. Participants viewed pairs of stimuli, and decided whether the second item was a real word or not. There were two variables, the semantic relationship between the prime and the target (related or unrelated) and the interval between the onset of prime and target (200 or 1000 ms), to vary the degree of semantic expectancy that was possible during task performance. Behavioural results replicated the well-established finding that identification of the target is facilitated by a preceding semantically related prime. Event-related functional magnetic resonance imaging (efMRI) identified two brain areas involved in the semantic-priming effect. Activity in the anterior medial temporal cortex was diminished when target words were primed by semantically related words, suggesting involvement of this brain region during active semantic association or integration. In contrast, activity in the left supramarginal gyrus in the temporal-parietal junction was enhanced for target words primed by semantically related words. Brain areas influenced by the interval between prime and target words, and by the interaction between word interval and semantic priming were also identified. A parallel experiment using event-related potentials (ERPs) unveiled a striking difference in the time course of semantic priming as a function of expectancy. In line with previous reports, the primary effect of semantic priming on ERPs was the attenuation of the N400 component, in both short- and long-interval conditions. However, the priming effect started significantly earlier in the long-interval condition. Activity in the anterior medial temporal cortex has previously been shown to contribute to the N400 component, a finding that links the priming results obtained with efMRI and ERP methods.

This paper considers how functional neuro-imaging can be used to investigate the organization of the semantic system and the limitations associated with this technique. The majority of the functional imaging studies of the semantic system have looked for divisions by varying stimulus category. These studies have led to divergent results and no clear anatomical hypotheses have emerged to account for the dissociations seen in behavioral studies. Only a few functional imaging studies have used task as a variable to differentiate the neural correlates of semantic features more directly. We extend these findings by presenting a new study that contrasts tasks that differentially weight sensory (color and taste) and verbally learned (origin) semantic features. Irrespective of the type of semantic feature retrieved, a common semantic system was activated as demonstrated in many previous studies. In addition, the retrieval of verbally learned, but not sensory-experienced, features enhanced activation in medial and lateral posterior parietal areas. We attribute these "verbally learned" effects to differences in retrieval strategy and conclude that evidence for segregation of semantic features at an anatomical level remains weak. We believe that functional imaging has the potential to increase our understanding of the neuronal infrastructure that sustains semantic processing but progress may require multiple experiments until a consistent explanatory framework emerges.

In this study we demonstrate the importance of distributed sampling of peristimulus time in blocked design fMRI studies. Distributed sampling ensures all the components of an event-related hemodynamic response are sampled and avoids the bias incurred when stimulus presentation is time-locked to data acquisition. We found that differences in the temporal offset between stimulus presentation and data acquisition had a significant effect on some language-related activations. These effects, induced by simply shifting stimulus presentation by a fraction of the interscan interval, suggest that fixed sampling does indeed bias estimated responses, even in blocked designs.

This article is about intersubject variability in the functional integration of activity in different brain regions. Previous studies of functional and effective connectivity have dealt with intersubject variability by analyzing data from different subjects separately or pretending the data came from the same subject. These approaches do not allow one to test for differences among subjects. The aim of this work was to illustrate how differences in connectivity among subjects can be addressed explicitly using structural equation modeling. This is enabled by constructing a multisubject network that comprises m regions of interest for each of the n subjects studied, resulting in a total of m x n nodes. Constructing a network of regions from different subjects may seem counterintuitive but embodies two key advantages. First, it allows one to test directly for differences among subjects by comparing models that do and do not allow a particular connectivity parameter to vary over subjects. Second, a multisubject network provides additional degrees of freedom to estimate the model's free parameters. Any neurobiological hypothesis normally addressed by single-subject or group analyses can still be tested, but with greater sensitivity. The common influence of experimental variables is modeled by connecting a virtual node, whose time course reflects stimulus onsets, to the sensory or "input" region in all subjects. Further experimental changes in task or cognitive set enter through modulation of the connections. This approach allows one to model both endogenous (or intrinsic) variance and exogenous effects induced by experimental design. We present a functional magnetic resonance imaging study that uses a multisubject network to investigate intersubject variability in functional integration in the context of single word and pseudoword reading. We tested whether the effect of word type on the reading-related coupling differed significantly among subjects. Our results showed that a number of forward and backward connections were stronger for reading pseudowords than words, and, in one case, connectivity showed significant intersubject variability. The discussion focuses on the implications of our findings and on further applications of the multisubject network analysis.

Background and Purpose: The increase in fractional anisotropy (FA) in acute stroke has yet to be explained. Using an engineering methodology known as pq diagrams, we sought to explain the increase in FA by describing changes in the total magnitude of the diffusion tensor (L) as well as the isotropic (p) and anisotropic (q) components., Methods: Diffusion tensor imaging was performed in 10 patients with stroke <27 hours old. The diffusion tensor was decomposed into the p and q components and plotted to describe the diffusion trajectories. FA was also calculated and compared., Results: There was significant and consistent reduction in p, q, and L (p: mean, -50.0%; range, -36.6% to -64.5%; q: mean, -50.8%; range, -30.8% to -72.8%; L: mean, -50.3%; range, -37.0% to -65.1%). There were inconsistent changes in FA (mean, -0.5%; range, -44.9% to +45.0%). Five patients had elevated FA due to proportionately higher loss of L than q., Conclusions: Changes in FA only occur when there is a change in the ratio of q/L. Acute elevation of FA occurred in the context of a larger reduction in L than q. The elevation in FA occurs in the context of a reduction in the anisotropic tensor and therefore is a consequence of ratio-metric measurement. This appears to clarify the reported increase in FA in terms of alterations in the shape of the apparent diffusion tensor. pq diagrams appear to offer improved resolution of acute diffusion changes in ischemia.

Patients with semantic impairments sometimes demonstrate category-specific deficits suggesting that the anatomical substrates of semantic memory may reflect categorical organisation, however, neuroimaging studies have failed to provide consistent data in support of a category-based account. We conducted three functional neuroimaging experiments to investigate the neural correlates of semantic processing, two with positron emission tomography (PET) and a third with functional magnetic resonance imaging (fMRI). The first experiment used a lexical decision task to search for brain regions selectively activated by concepts from four different categories--animals, fruit, tools, and vehicles. The second experiment used a semantic categorisation task to increase the demands on the semantic system and to look for evidence of consistent activations for the domains of natural kinds or man-made items. The final experiment was a replication of the semantic categorisation task using fMRI to increase the spatial resolution and statistical sensitivity of the experiment. The results of these experiments reliably identified a distributed neural system common to both natural kinds and artifacts but failed to find robust evidence of functional segregation by domain or categories. Category effects were neither reliable nor consistently present across experiments although some were consistent with previous studies. We discuss the implications of these findings, arguing that they are most consistent with a semantic system undifferentiated by category at the neural level.

We introduce a new method for detecting differences in the latency of blood oxygenation level-dependent (BOLD) responses to brief events within the context of the General Linear Model. Using a first-order Taylor approximation in terms of the temporal derivative of a canonical hemodynamic response function, statistical parametric maps of differential latencies were estimated via the ratio of derivative to canonical parameter estimates. This method was applied to two example datasets: comparison of words versus nonwords in a lexical decision task and initial versus repeated presentations of faces in a fame-judgment task. Tests across subjects revealed both magnitude and latency differences within several brain regions. This approach offers a computationally efficient means of detecting BOLD latency differences over the whole brain. Precise characterization of the hemodynamic latency and its interpretation in terms of underlying neural differences remain problematic, however.

The aim of this work was to investigate the dependence of BOLD responses on different patterns of stimulus input/neuronal changes. In an earlier report, we described an input-state-output model that combined (i) the Balloon/Windkessel model of nonlinear coupling between rCBF and BOLD signals, and (ii) a linear model of how regional flow changes with synaptic activity. In the present investigation, the input-state-output model was used to explore the dependence of simulated PET (rCBF) and fMRI (BOLD) signals on various parameters pertaining to experimental design. Biophysical simulations were used to estimate rCBF and BOLD responses as functions of (a) a prior stimulus, (b) epoch length (for a fixed SOA), (c) SOA (for a fixed number of events), and (d) stimulus amplitude. We also addressed the notion that a single neuronal response may differ, in terms of the relative contributions of early and late neural components, and investigated the effect of (e) the relative size of the late or "endogenous" neural component. We were interested in the estimated average rCBF and BOLD responses per stimulus or event, not in the statistical efficiency with which these responses are detected. The BOLD response was underestimated relative to rCBF with a preceding stimulus, increasing epoch length, and increasing SOA. Furthermore, the BOLD response showed some highly nonlinear behaviour when varying stimulus amplitude, suggesting some form of hemodynamic "rectification." Finally, the BOLD response was underestimated in the context of large late neuronal components. The difference between rCBF and BOLD is attributed to the nonlinear transduction of rCBF to BOLD signal. Our simulations support the idea that varying parameters that specify the experimental design may have differential effects in PET and fMRI. Moreover, they show that fMRI can be asymmetric in its ability to detect deactivations relative to activations when an absolute baseline is stipulated. Finally, our simulations suggest that relative insensitivity to BOLD signal in specific regions, such as the temporal lobe, may be partly explained by higher cognitive functions eliciting a relatively large late endogenous neuronal component., (Copyright 2001 Academic Press.)

Recent advances in MR diffusion weighted imaging (DWI) enable the identification of anisotropic white matter tracts with diffusion tensor imaging (DTI). We aimed to use a novel DTI technique to safely study patients with recent stroke in a high field (3 T) MR machine with its intrinsically higher spatial resolution and signal-to-noise ratio. Of ten patients studied, six had disruption of white matter tracts as determined by DTI. A further patient had distortion of white matter tracts around an infarct rather than actual disruption of the tracts themselves. The lack of tract destruction may imply a beneficial prognosis, information that is not available with conventional DWI.

In this paper, we present a post hoc method for identifying regions where functional MRI data are subject to signal reduction that may compromise sensitivity to activations. The motivation for developing this technique derives from recent language studies that showed responses in the inferior temporal lobe that could be detected by PET but not by fMRI. Reduced signal is due mostly to susceptibility artifacts and can be identified by comparing the T2* images (which are subject to susceptibility artifacts) with T2 images (which are not). However, T2 images are not usually acquired in fMRI studies. Therefore, we propose that areas with reduced signal can be identified by comparing T2* images that are corrected for nonuniformity with the original uncorrected images. The technique provides a voxel-wise characterization of signal reduction that pertains to the particular data that enter into a statistical model. It requires only the functional data and can thus be applied post hoc and without any additional scans., (Copyright 2001 Academic Press.)

There is a growing appreciation of the importance of nonlinearities in evoked responses in fMRI, particularly with the advent of event-related fMRI. These nonlinearities are commonly expressed as interactions among stimuli that can lead to the suppression and increased latency of responses to a stimulus that are incurred by a preceding stimulus. We have presented previously a model-free characterization of these effects using generic techniques from nonlinear system identification, namely a Volterra series formulation. At the same time Buxton et al. (1998) described a plausible and compelling dynamical model of hemodynamic signal transduction in fMRI. Subsequent work by Mandeville et al. (1999) provided important theoretical and empirical constraints on the form of the dynamic relationship between blood flow and volume that underpins the evolution of the fMRI signal. In this paper we combine these system identification and model-based approaches and ask whether the Balloon model is sufficient to account for the nonlinear behaviors observed in real time series. We conclude that it can, and furthermore the model parameters that ensue are biologically plausible. This conclusion is based on the observation that the Balloon model can produce Volterra kernels that emulate empirical kernels. To enable this evaluation we had to embed the Balloon model in a hemodynamic input-state-output model that included the dynamics of perfusion changes that are contingent on underlying synaptic activation. This paper presents (i) the full hemodynamic model (ii), how its associated Volterra kernels can be derived, and (iii) addresses the model's validity in relation to empirical nonlinear characterizations of evoked responses in fMRI and other neurophysiological constraints., (Copyright 2000 Academic Press.)

In this paper we report three neuroimaging studies of language that investigate potential sources of inconsistency in measured hemodynamic responses: (1) between sessions for fMRI, including differences in hormonal status, (2) between sessions for PET, and (3) between scanning modalities (PET and fMRI). Differences in evoked responses between sessions of the same modality were small. In particular we did not find any effect of hormone levels when testing during the first and third weeks of the menstrual cycle (although we cannot exclude the possibility that activation in the temporoparietal regions is sensitive to hormonal status). Comparing the two modalities showed that prefrontal regions were more activated in fMRI than in PET. This may relate to task switching between blocks in fMRI that is not induced by PET paradigms or increased error variance in these regions for PET. In contrast, temporal activations were found in PET more than in fMRI. We attribute the lack of temporal activations, in fMRI, to a combination of factors, including susceptibility artifacts, anticipatory activity during the control condition, discontinuous sampling of peristimulus time, and differences in the source, acquisition, and analysis of the measured signals. It is concluded that although there is sufficient reproducibility of results for these paradigms within each modality, the regionally specific differences in sensitivity found between modalities warrant further investigation. These regionally specific differences are important for a properly qualified interpretation of activation profiles in fMRI., (Copyright 2000 Academic Press.)

In this paper we present an approach to making inferences about generic activations in groups of subjects using fMRI. In particular we suggest that activations common to all subjects reflect aspects of functional anatomy that may be "typical" of the population from which that group was sampled. These commonalities can be identified by a conjunction analysis of the activation effects in which the contrasts, testing for an activation, are specified separately for each subject. A conjunction is the joint refutation of multiple null hypotheses, in this instance, of no activation in any subject. The motivation behind this use of conjunctions is that fixed-effect analyses are generally more "sensitive" than equivalent random-effect analyses. This is because fixed-effect analyses can harness the large degrees of freedom and small scan-to-scan variability (relative to the variability in responses from subject to subject) when assessing the significance of an estimated response. The price one pays for the apparent sensitivity of fixed-effect analyses is that the ensuing inferences pertain to, and only to, the subjects studied. However, a conjunction analysis, using a fixed-effect model, allows one to infer: (i) that every subject studied activated and (ii) that at least a certain proportion of the population would have shown this effect. The second inference depends upon a meta-analytic formulation in terms of a confidence region for this proportion. This approach retains the sensitivity of fixed-effect analyses when the inference that only a substantial proportion of the population activates is sufficient., (Copyright 1999 Academic Press.)

This paper concerns the experimental design and statistical models employed by fMRI activation studies which block presentation of linguistic stimuli. In particular, we note that the relationship between the timing of stimulus presentation and data acquisition can have a substantial impact on the ability to detect activations in critical language areas, even when the stimuli are presented in blocks. Using a blocked word rhyming paradigm and repeated investigations on a single subject, activation was observed in Broca's area (left inferior frontal cortex) and Wernicke's area (left posterior temporoparietal cortex) when (i) the timing of data acquisition was distributed throughout the peristimulus time and (ii) an event-related analysis was used to model the phasic nature of the hemodynamic response within each block of repeated word stimuli. In contrast, when the timing of data acquisition relative to stimulus presentation was fixed, activation was detected in Broca's area but not consistently in Wernicke's area. Our results indicate that phasic responses to stimuli occur even in a blocked design and that the sampling and proper modeling of these responses can have profound effects on their detection. Specifically, distributed sampling over peristimulus time is essential in order to detect small activations particularly when they are transient. These findings are likely to generalize to the detection of transient signals in any cognitive paradigm., (Copyright 1999 Academic Press.)

Being able to accurately quantify the hemodynamic response function (HRF) that links the blood oxygen level dependent functional magnetic resonance imaging (BOLD-fMRI) signal to the underlying neural activity is important both for elucidating neurovascular coupling mechanisms and improving the accuracy of fMRI-based functional connectivity analyses. In particular, HRF estimation using BOLD-fMRI is challenging particularly in the case of resting-state data, due to the absence of information about the underlying neuronal dynamics. To this end, using simultaneously recorded electroencephalography (EEG) and fMRI data is a promising approach, as EEG provides a more direct measure of neural activations. In the present work, we employ simultaneous EEG-fMRI to investigate the regional characteristics of the HRF using measurements acquired during resting conditions. We propose a novel methodological approach based on combining distributed EEG source space reconstruction, which improves the spatial resolution of HRF estimation and using block-structured linear and nonlinear models, which enables us to simultaneously obtain HRF estimates and the contribution of different EEG frequency bands. Our results suggest that the dynamics of the resting-state BOLD signal can be sufficiently described using linear models and that the contribution of each band is region specific. Specifically, it was found that sensory-motor cortices exhibit positive HRF shapes, whereas the lateral occipital cortex and areas in the parietal cortex, such as the inferior and superior parietal lobule exhibit negative HRF shapes. To validate the proposed method, we repeated the analysis using simultaneous EEG-fMRI measurements acquired during execution of a unimanual hand-grip task. Our results reveal significant associations between BOLD signal variations and electrophysiological power fluctuations in the ipsilateral primary motor cortex, particularly for the EEG beta band, in agreement with previous studies in the literature., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

This paper is about detecting activations in statistical parametric maps and considers the relative sensitivity of a nested hierarchy of tests that we have framed in terms of the level of inference (voxel level, cluster level, and set level). These tests are based on the probability of obtaining c, or more, clusters with k, or more, voxels, above a threshold u. This probability has a reasonably simple form and is derived using distributional approximations from the theory of Gaussian fields. The most important contribution of this work is the notion of set-level inference. Set-level inference refers to the statistical inference that the number of clusters comprising an observed activation profile is highly unlikely to have occurred by chance. This inference pertains to the set of activations reaching criteria and represents a new way of assigning P values to distributed effects. Cluster-level inferences are a special case of set-level inferences, which obtain when the number of clusters c = 1. Similarly voxel-level inferences are special cases of cluster-level inferences that result when the cluster can be very small (i.e., k = 0). Using a theoretical power analysis of distributed activations, we observed that set-level inferences are generally more powerful than cluster-level inferences and that cluster-level inferences are generally more powerful than voxel-level inferences. The price paid for this increased sensitivity is reduced localizing power: Voxel-level tests permit individual voxels to be identified as significant, whereas cluster-and set-level inferences only allow clusters or sets of clusters to be so identified. For all levels of inference the spatial size of the underlying signal f (relative to resolution) determines the most powerful thresholds to adopt. For set-level inferences if f is large (e.g., fMRI) then the optimum extent threshold should be greater than the expected number of voxels for each cluster. If f is small (e.g., PET) the extent threshold should be small. We envisage that set-level inferences will find a role in making statistical inferences about distributed activations, particularly in fMRI.

The medial temporal lobe (MTL) is the core neural construction related to associative memory. This study sought to explore the dynamic functional connectivity (dFC) between the subdivisions of MTL and other regions in the whole brain. Additionally, it sought to determine relationships between connectivity stability and associative memory function, to elucidate the neural foundation of associative memory from the perspectives of dFC. A Wechsler Memory Scale China revised edition (WMS-RC) measurement and a resting-state functional magnetic resonance imaging were conducted to clarify adults' function of associative memory and dFC patterns in subdivisions of the MTL. A multiple regression analysis was carried out to analyze the relationships described above. The results demonstrated that (i) connectivity in the left brain included the anterior hippocampus (aHIP) and right fusiform (Fusiform&#95;R), middle hippocampus (mHIP) and right inferior parietal lobule (IPL&#95;R), posterior hippocampus (pHIP) and left inferior parietal lobule (IPL&#95;L), perirhinal cortex (PRC) and left supramarginal gyrus (SMG&#95;L), entorhinal cortex (ERC) and [left middle temporal gyrus (MTG&#95;L), left superior parietal lobule (SPL&#95;L), right fusiform (Fusiform&#95;R)], anterior parahippocampal cortex (aPHC) and right precentral gyrus (PCG&#95;R); (ii) connectivity in the right brain included the aHIP and right supramarginal gyrus (SMG&#95;R), mHIP and left paracentral lobule (PCL&#95;L), pHIP and left superior occipital gyrus (SOG&#95;L), PRC and left middle occipital gyrus (MOG&#95;L), ERC and right middle occipital gyrus (MOG&#95;R); (iii) for most connectivity patterns, the more stable the dFC, the better are the associative memory functions. This study elucidates the neural foundations of associative memory in terms of dFC patterns., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Purpose: This study aimed to investigate how the functional homotopy and further functional connectivity (FC) of whole brain changed in temporal lobe epilepsy (TLE). We also evaluated which brain regions played a decisive role in classification by using functional magnetic resonance imaging (fMRI)., Methods: Patients with TLE and matched healthy controls were included to collect the fMRI data and perform the voxel-mirrored homotopic connectivity (VMHC) and FC analyses. The correlation between the changed functional homotopy and neuropsychology tests was examined. Based on VMHC, the weight of each region in the classification was obtained using multivariate pattern analysis (MVPA)., Results: The patients exhibited decreased functional coordination in the bilateral inferior temporal gyrus (ITG) and increased functional homotopy in the bilateral lingual gyrus compared with the control group in the VMHC analysis. Compared with healthy controls, the Montreal Cognitive Assessment score was lower, and the scores of Hamilton Anxiety (HAMA) and Hamilton Depression Scales were higher. The score of the HAMA Scale was positively correlated with the altered bilateral ITG. The FC analysis revealed increased connections between the right lingual gyrus and the left superior temporal gyrus/left insula. The MVPA showed that the accuracy, sensitivity, and specificity of classification were 68.49, 66.67 and 70.27%, respectively, and it confirmed that the temporal lobe, cerebellum, and parietal lobe provided significant contributions., Conclusion: These findings demonstrated that the VMHC and FC changed in TLE, and the alterations were correlated with the anxiety state. The MVPA indicated that the abnormal VMHC was a crucial fMRI feature., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)