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Introduction: The relationship between neural social cognition patterns and performance on social cognition tasks in daily life is a topic of debate, with key consideration given to the extent to which theory of mind (ToM) brain circuits share properties reflecting everyday social functioning. To test the efficacy of ecological stimuli in eliciting brain activation within the ToM brain circuits, we adapted the Edinburgh Social Cognition test social scenarios, consisting of dynamic ecological contextually embedded social stimuli, to a fMRI paradigm. Methods: Forty-two adults (21 men, mean age ± SD = 34.19 years ±12.57) were enrolled and underwent an fMRI assessment which consisted of a ToM task using the Edinburgh Social Cognition test scenarios. We used the same stimuli to prompt implicit (movie viewing) and explicit (silent and two-choice answers) reasoning on cognitive and affective mental states. The fMRI analysis was based on the classical random effect analysis. Group inferences were complemented with supplemental analyses using overlap maps to assess inter-subject variability. Results: We found that explicit mentalizing reasoning yielded wide neural activations when two-choice answers were used. We also observed that the nature of ToM reasoning, that is, affective or cognitive, played a significant role in activating different neural circuits. Discussion: The ESCoT stimuli were particularly effective in evoking ToM core neural underpinnings and elicited executive frontal loops. Future work may employ the task in a clinical setting to investigate ToM network reorganization and plasticity. [ABSTRACT FROM AUTHOR]

Introduction: Functional magnetic resonance imaging (fMRI) has become a fundamental tool for studying brain function. However, the presence of serial correlations in fMRI data complicates data analysis, violates the statistical assumptions of analyses methods, and can lead to incorrect conclusions in fMRI studies. Methods: In this paper, we show that conventional whitening procedures designed for data with longer repetition times (TRs) (>2 s) are inadequate for the increasing use of short-TR fMRI data. Furthermore, we comprehensively investigate the shortcomings of existing whitening methods and introduce an iterative whitening approach named "IDAR" (Iterative Data-adaptive Autoregressivemodel) to address these shortcomings. IDAR employs high-order autoregressive (AR) models with flexible and data-driven orders, offering the capability to model complex serial correlation structures in both short-TR and long-TR fMRI datasets. Results: Conventional whiteningmethods, such as AR(1), ARMA(1,1), and higher-order AR, were effective in reducing serial correlation in long-TR data but were largely ineffective in even reducing serial correlation in short-TR data. In contrast, IDAR significantly outperformed conventional methods in addressing serial correlation, power, and Type-I error for both long-TR and especially short-TR data. However, IDAR could not simultaneously address residual correlations and inflated Type-I error effectively. Discussion: This study highlights the urgent need to address the problem of serial correlation in short-TR (<1 s) fMRI data, which are increasingly used in the field. Although IDAR can address this issue for a wide range of applications and datasets, the complexity of short-TR data necessitates continued exploration and innovative approaches. These efforts are essential to simultaneously reduce serial correlations and control Type-I error rates without compromising analytical power. [ABSTRACT FROM AUTHOR]

The human brain is composed of multiple, discrete, functionally specialized regions that are interconnected to form large-scale distributed networks. Using advanced brain-imaging methods and machine-learning analytical approaches, recent studies have demonstrated that regional brain activity during the performance of various cognitive tasks can be accurately predicted from patterns of task-independent brain connectivity. In this review article, we first present evidence for the predictability of brain activity from structural connectivity (i.e., white matter connections) and functional connectivity (i.e., temporally synchronized task-free activations). We then discuss the implications of such predictions to clinical populations, such as patients diagnosed with psychiatric disorders or neurologic diseases, and to the study of brain–behavior associations. We conclude that connectivity may serve as an infrastructure that dictates brain activity, and we pinpoint several open questions and directions for future research. [ABSTRACT FROM AUTHOR]

Resting-state of the human brain has been described by a combination of various basis modes including the default mode network (DMN) identified by fMRI BOLD signals in human brains. Whether DMN is the most dominant representation of the resting-state has been under question. Here, we investigated the unexplored yet fundamental nature of the resting-state. In the absence of global signal regression for the analysis of brain-wide spatial activity pattern, the fMRI BOLD spatiotemporal signals during the rest were completely decomposed into time-invariant spatial-expression basis modes (SEBMs) and their time-evolution basis modes (TEBMs). Contrary to our conventional concept above, similarity clustering analysis of the SEBMs from 166 human brains revealed that the most dominant SEBM cluster is an asymmetric mode where the distribution of the sign of the components is skewed in one direction, for which we call essential mode (EM), whereas the second dominant SEBM cluster resembles the spatial pattern of DMN. Having removed the strong 1/f noise in the power spectrum of TEBMs, the genuine oscillatory behavior embedded in TEBMs of EM and DMN-like mode was uncovered around the low-frequency range below 0.2 Hz.

IntroductionThe relationship between neural social cognition patterns and performance on social cognition tasks in daily life is a topic of debate, with key consideration given to the extent to which theory of mind (ToM) brain circuits share properties reflecting everyday social functioning. To test the efficacy of ecological stimuli in eliciting brain activation within the ToM brain circuits, we adapted the Edinburgh Social Cognition test social scenarios, consisting of dynamic ecological contextually embedded social stimuli, to a fMRI paradigm.MethodsForty-two adults (21 men, mean age ± SD = 34.19 years ±12.57) were enrolled and underwent an fMRI assessment which consisted of a ToM task using the Edinburgh Social Cognition test scenarios. We used the same stimuli to prompt implicit (movie viewing) and explicit (silent and two-choice answers) reasoning on cognitive and affective mental states. The fMRI analysis was based on the classical random effect analysis. Group inferences were complemented with supplemental analyses using overlap maps to assess inter-subject variability.ResultsWe found that explicit mentalizing reasoning yielded wide neural activations when two-choice answers were used. We also observed that the nature of ToM reasoning, that is, affective or cognitive, played a significant role in activating different neural circuits.DiscussionThe ESCoT stimuli were particularly effective in evoking ToM core neural underpinnings and elicited executive frontal loops. Future work may employ the task in a clinical setting to investigate ToM network reorganization and plasticity.

Hypokinetic dysarthria (HD) is a difficult-to-treat symptom affecting quality of life in patients with Parkinson's disease (PD). Levodopa may partially alleviate some symptoms of HD in PD, but the neural correlates of these effects are not fully understood. The aim of our study was to identify neural mechanisms by which levodopa affects articulation and prosody in patients with PD. Altogether 20 PD patients participated in a task fMRI study (overt sentence reading). Using a single dose of levodopa after an overnight withdrawal of dopaminergic medication, levodopa-induced BOLD signal changes within the articulatory pathway (in regions of interest; ROIs) were studied. We also correlated levodopa-induced BOLD signal changes with the changes in acoustic parameters of speech. We observed no significant changes in acoustic parameters due to acute levodopa administration. After levodopa administration as compared to the OFF dopaminergic condition, patients showed task-induced BOLD signal decreases in the left ventral thalamus (p = 0.0033). The changes in thalamic activation were associated with changes in pitch variation (R = 0.67, p = 0.006), while the changes in caudate nucleus activation were related to changes in the second formant variability which evaluates precise articulation (R = 0.70, p = 0.003). The results are in line with the notion that levodopa does not have a major impact on HD in PD, but it may induce neural changes within the basal ganglia circuitries that are related to changes in speech prosody and articulation. [ABSTRACT FROM AUTHOR]

Reductions in the ability to encode and retrieve past experiences in rich spatial contextual detail (episodic memory) are apparent by midlife--a time when most females experience spontaneous menopause. Yet, little is known about how menopause status affects episodic memory-related brain activity at encoding and retrieval in middle-aged premenopausal and postmenopausal females, and whether any observed group differences in brain activity and memory performance correlate with chronological age within group. We conducted an event-related task fMRI study of episodic memory for spatial context to address this knowledge gap. Multivariate behavioral partial least squares was used to investigate how chronological age and retrieval accuracy correlated with brain activity in 31 premenopausal females (age range, 39.55-53.30 years; mean age, 44.28 years; SD age, 3.12 years) and 41 postmenopausal females (age range, 46.70-65.14 years; mean age, 57.56 years; SD age, 3.93 years). We found that postmenopausal status, and advanced age within postmenopause, was associated with lower spatial context memory. The fMRI analysis showed that only in postmenopausal females, advanced age was correlated with decreased activity in occipitotemporal, parahippocampal, and inferior parietal cortices during encoding and retrieval, and poorer spatial context memory performance. In contrast, only premenopausal females exhibited an overlap in encoding and retrieval activity in angular gyrus, midline cortical regions, and prefrontal cortex, which correlated with better spatial context retrieval accuracy. These results highlight how menopause status and chronological age, nested within menopause group, affect episodic memory and its neural correlates at midlife. [ABSTRACT FROM AUTHOR]

Focused attention meditation (FAM) training has been shown to improve attention, but the neural basis of FAM on attention has not been thoroughly understood. Here, we aim to investigate the neural effect of a 2-month FAM training on novice meditators in a visual oddball task (a frequently adopted task to evaluate attention), evaluated with both ASL and BOLD fMRI. Using ASL, activation was increased in the middle cingulate (part of the salience network, SN) and temporoparietal (part of the frontoparietal network, FPN) regions; the FAM practice time was negatively associated with the longitudinal changes in activation in the medial prefrontal (part of the default mode network, DMN) and middle frontal (part of the FPN) regions. Using BOLD, the FAM practice time was positively associated with the longitudinal changes of activation in the inferior parietal (part of the dorsal attention network, DAN), dorsolateral prefrontal (part of the FPN), and precentral (part of the DAN) regions. The effect sizes for the activation changes and their association with practice time using ASL are significantly larger than those using BOLD. Our study suggests that FAM training may improve attention via modulation of the DMN, DAN, SN, and FPN, and ASL may be a sensitive tool to study the FAM effect on attention. [ABSTRACT FROM AUTHOR]

There is a growing body of research showing that cerebral pathophysiological processes triggered by pediatric mild traumatic brain injury (pmTBI) may extend beyond the usual clinical recovery timeline. It is paramount to further unravel these processes, because the possible long‐term cognitive effects resulting from ongoing secondary injury in the developing brain are not known. In the current fMRI study, neural processes related to cognitive control were studied in 181 patients with pmTBI at sub‐acute (SA; ~1 week) and early chronic (EC; ~4 months) stages post‐injury. Additionally, a group of 162 age‐ and sex‐matched healthy controls (HC) were recruited at equivalent time points. Proactive (post‐cue) and reactive (post‐probe) cognitive control were examined using a multimodal attention fMRI paradigm for either congruent or incongruent stimuli. To study brain network function, the triple‐network model was used, consisting of the executive and salience networks (collectively known as the cognitive control network), and the default mode network. Additionally, whole‐brain voxel‐wise analyses were performed. Decreased deactivation was found within the default mode network at the EC stage following pmTBI during both proactive and reactive control. Voxel‐wise analyses revealed sub‐acute hypoactivation of a frontal area of the cognitive control network (left pre‐supplementary motor area) during proactive control, with a reversed effect at the EC stage after pmTBI. Similar effects were observed in areas outside of the triple‐network during reactive control. Group differences in activation during proactive control were limited to the visual domain, whereas for reactive control findings were more pronounced during the attendance of auditory stimuli. No significant correlations were present between task‐related activations and (persistent) post‐concussive symptoms. In aggregate, current results show alterations in neural functioning during cognitive control in pmTBI up to 4 months post‐injury, regardless of clinical recovery. We propose that subacute decreases in activity reflect a general state of hypo‐excitability due to the injury, while early chronic hyperactivation represents a compensatory mechanism to prevent default mode interference and to retain cognitive control. [ABSTRACT FROM AUTHOR]

Risk-taking often occurs in childhood as a compex outcome influenced by individual, family, and social factors. The ability to govern risky decision-making in a balanced manner is a hallmark of the integrity of cognitive and affective development from childhood to adulthood. The Triadic Neural Systems Model posits that the nuanced coordination of motivational approach, avoidance and prefrontal control systems is crucial to regulate adaptive risk-taking and related behaviors. Although widely studied in adolescence and adulthood, how these systems develop in childhood remains elusive. Here, we show heterogenous age-related differences in the triadic neural systems involved in risky decision-making in 218 school-age children relative to 80 young adults. Children were generally less reward-seeking and less risk-taking than adults, and exhibited gradual increases in risk-taking behaviors from 6 to 12 years-old, which are associated with age-related differences in brain activation patterns underlying reward and risk processing. In comparison to adults, children exhibited weaker activation in control-related prefrontal systems, but stronger activation in reward-related striatal systems. Network analyses revealed that children showed greater reward-related functional connectivity within and between the triadic systems. Our findings support an immature and unbalanced developmental view of the core neurocognitive systems involved in risky decision-making and related behaviors in middle to late childhood.

PurposefMRI is increasingly used for presurgical language mapping, but lack of standard methodology has made it difficult to combine/compare data across institutions or determine the relative efficacy of different approaches. Here, we describe a quantitative analytic framework for determining language laterality in clinical fMRI that addresses these concerns.MethodsWe retrospectively analyzed fMRI data from 59 patients who underwent presurgical language mapping at our institution with identical imaging and behavioral protocols. First, we compared the efficacy of different regional masks in capturing language activations. Then, we systematically explored how laterality indices (LIs) computed from these masks vary as a function of task and activation threshold. Finally, we determined the percentile threshold that maximized the correlation between the results of our LI approach and the laterality assessments from the original clinical radiology reports.ResultsFirst, we found that a regional mask derived from a meta-analysis of the fMRI literature better captured language task activations than masks based on anatomically defined language areas. Then, we showed that an LI approach based on this functional mask and percentile thresholding of subject activation can quantify the relative ability of different language tasks to lateralize language function at the population level. Finally, we determined that the 92nd percentile of subject-level activation provides the optimal LI threshold with which to reproduce the original clinical reports.ConclusionA quantitative framework for determining language laterality that uses a functionally-derived language mask and percentile thresholding of subject activation can combine/compare results across tasks and patients and reproduce clinical assessments of language laterality.

How to retrieve latent neurobehavioural processes from complex neurobiological signals is an important yet unresolved challenge. Here, we develop a novel approach, orthogonal-Decoding multi-Cognitive Processes (DeCoP), to reveal underlying latent neurobehavioural processing and show that its performance is superior to traditional non-orthogonal decoding in terms of both false inference and robustness. Processing value and salience information are two fundamental but mutually confounded pathways of reward reinforcement essential for decision making. During reward/punishment anticipation, we applied DeCoP to decode brain-wide responses into spatially overlapping, yet functionally independent, evaluation and readiness processes, which are modulated differentially by meso‑limbic vs nigro-striatal dopamine systems. Using DeCoP, we further demonstrated that most brain regions only encoded abstract information but not the exact input, except for dorsal anterior cingulate cortex and insula. Furthermore, we anticipate our novel analytical principle to be applied generally in decoding multiple latent neurobehavioral processes and thus advance both the design and hypothesis testing for cognitive tasks.

Rationale: Emotion regulation (ER) difficulties have been previously described in bipolar disorder (BD). Whilst lithium has been shown to be effective in the treatment of BD, the mechanisms underlying lithium's effect on mood stabilisation remain unclear. Objectives: Unravelling lithium's effect on psychological processes impaired in BD, such as ER, could address this translational gap and inform the development of new treatments. Methods: This study investigated the neural effects of lithium (800mg) on ER in 33 healthy volunteers in a double-blind between-groups design, randomised to lithium (n=17) or placebo (n=16) for 11 days. At treatment completion, participants underwent 3-Tesla fMRI scan whilst performing an ER task. Results: Reappraisal reduced negative affect across groups and led to the expected increase in frontal brain activity. Participants receiving lithium showed (1) decreased activation in prefrontal and posterior parietal cortices and connectivity between the fronto-limbic network (Z>2.3, p<0.05 corrected); and (2) increased activity in the right superior temporal gyrus (Z>3.1, p<0.05 corrected) and connectivity between the right medial temporal gyrus (MTG) and left middle frontal gyrus (Z>2.3, p<0.05 corrected) during reappraisal. Further effects of lithium were found in response to negative picture presentation, whereby an anticorrelation was found between the left amygdala and the frontal cortex, and greater connectivity between the right MTG and the bilateral medial prefrontal cortex extending into the paracingulate gyrus, compared to placebo (Z>2.3, p < 0.05 corrected). Conclusions: These results show a potential effect of lithium on ER through its effects on activity and connectivity, and further elaborate the neural underpinnings of cognitive reappraisal. Future work should investigate longer term effects of lithium on ER in BD, ultimately benefitting the development of novel and more effective treatments. [ABSTRACT FROM AUTHOR]

The investigation of functional brain networks (FBNs) using task-based functional magnetic resonance imaging (tfMRI) has gained significant attention in the field of neuroimaging. Despite the availability of several methods for constructing FBNs, including traditional methods like GLM and deep learning methods such as spatiotemporal self-attention mechanism (STAAE), these methods have design and training limitations. Specifically, they do not consider the intrinsic characteristics of fMRI data, such as the possibility that the same signal value at different time points could represent different brain states and meanings. Furthermore, they overlook prior knowledge, such as task designs, during training. This study aims to overcome these limitations and develop a more efficient model by drawing inspiration from techniques in the field of natural language processing (NLP). The proposed model, called the Multi-head Attention-based Masked Sequence Model (MAMSM), uses a multi-headed attention mechanism and mask training approach to learn different states corresponding to the same voxel values. Additionally, it combines cosine similarity and task design curves to construct a novel loss function. The MAMSM was applied to seven task state datasets from the Human Connectome Project (HCP) tfMRI dataset. Experimental results showed that the features acquired by the MAMSM model exhibit a Pearson correlation coefficient with the task design curves above 0.95 on average. Moreover, the model can extract more meaningful networks beyond the known task-related brain networks. The experimental results demonstrated that MAMSM has great potential in advancing the understanding of functional brain networks. [ABSTRACT FROM AUTHOR]

Recent evidence suggests that, in the absence of any task, spontaneous brain activity patterns and connectivity in the visual and motor cortex code for natural stimuli and actions, respectively. These “resting-state” activity patterns may underlie the maintenance and consolidation (replay) of information states coding for ecological stimuli and behaviors. In this study, we examine whether replay patterns occur in resting-state activity in association cortex grouped into high-order cognitive networks not directly processing sensory inputs or motor outputs. Fifteen participants (7 females) performed four hand movements during an fMRI study. Three movements were ecological. The fourth movement as control was less ecological. Before and after the task scans, we acquired resting-state fMRI scans. The analysis examined whether multivertex task activation patterns for the four movements computed at the cortical surface in different brain networks resembled spontaneous activity patterns measured at rest. For each movement, we computed a vector of r values indicating the strength of the similarity between the mean task activation pattern and frame-by-frame resting-state patterns. We computed a cumulative distribution function of r² values and used the 90th percentile cutoff value for comparison. In the dorsal attention network, resting-state patterns were more likely to match task patterns for the ecological movements than the control movement. In contrast, resttask pattern correlation was more likely for less ecological movement in the ventral attention network. These findings show that spontaneous activity patterns in human attention networks code for hand movements. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) affects the language processes, with a significant impact on the patients' daily communication. We aimed to describe specific alterations in the comprehension of syntactically complex sentences in patients with PD (PwPD) as compared to healthy controls (HC) and to identify the neural underpinnings of these deficits using a functional connectivity analysis of the striatum. A total of 20 patients PwPD and 15 HC participated in the fMRI study. We analyzed their performance of a Test of sentence comprehension (ToSC) adjusted for fMRI. A task-dependent functional connectivity analysis of the striatum was conducted using the psychophysiological interaction method (PPI). On the behavioral level, the PwPD scored significantly lower (mean sd: 77.3 12.6) in the total ToSC score than the HC did (mean sd: 86.6 8.0), p = 0.02, and the difference was also significant specifically for sentences with a noncanonical word order (PD-mean sd: 69.9 14.1, HC-mean sd: 80.2 11.5, p = 0.04). Using PPI, we found a statistically significant difference between the PwPD and the HC in connectivity from the right striatum to the supplementary motor area [SMA, (4 8 53)] for non-canonical sentences. This PPI connectivity was negatively correlated with the ToSC accuracy of non-canonical sentences in the PwPD. Our results showed disturbed sentence reading comprehension in the PwPD with altered task-dependent functional connectivity from the right striatum to the SMA, which supports the synchronization of the temporal and sequential aspects of language processing. The study revealed that subcorticalcortical networks (striatal-frontal loop) in PwPD are compromised, leading to impaired comprehension of syntactically complex sentences. [ABSTRACT FROM AUTHOR]

Visual perception is abnormal in psychotic disorders such as schizophrenia. In addition to hallucinations, laboratory tests show differences in fundamental visual processes including contrast sensitivity, center-surround interactions, and perceptual organization. A number of hypotheses have been proposed to explain visual dysfunction in psychotic disorders, including an imbalance between excitation and inhibition. However, the precise neural basis of abnormal visual perception in people with psychotic psychopathology (PwPP) remains unknown. Here, we describe the behavioral and 7 tesla MRI methods we used to interrogate visual neurophysiology in PwPP as part of the Psychosis Human Connectome Project (HCP). In addition to PwPP (n = 66) and healthy controls (n = 43), we also recruited first-degree biological relatives (n = 44) in order to examine the role of genetic liability for psychosis in visual perception. Our visual tasks were designed to assess fundamental visual processes in PwPP, whereas MR spectroscopy enabled us to examine neurochemistry, including excitatory and inhibitory markers. We show that it is feasible to collect high-quality data across multiple psychophysical, functional MRI, and MR spectroscopy experiments with a sizable number of participants at a single research site. These data, in addition to those from our previously described 3 tesla experiments, will be made publicly available in order to facilitate further investigations by other research groups. By combining visual neuroscience techniques and HCP brain imaging methods, our experiments offer new opportunities to investigate the neural basis of abnormal visual perception in PwPP.

Parkinson’s disease (PD) affects the language processes, with a significant impact on the patients’ daily communication. We aimed to describe specific alterations in the comprehension of syntactically complex sentences in patients with PD (PwPD) as compared to healthy controls (HC) and to identify the neural underpinnings of these deficits using a functional connectivity analysis of the striatum. A total of 20 patients PwPD and 15 HC participated in the fMRI study. We analyzed their performance of a Test of sentence comprehension (ToSC) adjusted for fMRI. A task-dependent functional connectivity analysis of the striatum was conducted using the psychophysiological interaction method (PPI). On the behavioral level, the PwPD scored significantly lower (mean ± sd: 77.3 ± 12.6) in the total ToSC score than the HC did (mean ± sd: 86.6 ± 8.0), p = 0.02, and the difference was also significant specifically for sentences with a non-canonical word order (PD-mean ± sd: 69.9 ± 14.1, HC-mean ± sd: 80.2 ± 11.5, p = 0.04). Using PPI, we found a statistically significant difference between the PwPD and the HC in connectivity from the right striatum to the supplementary motor area [SMA, (4 8 53)] for non-canonical sentences. This PPI connectivity was negatively correlated with the ToSC accuracy of non-canonical sentences in the PwPD. Our results showed disturbed sentence reading comprehension in the PwPD with altered task-dependent functional connectivity from the right striatum to the SMA, which supports the synchronization of the temporal and sequential aspects of language processing. The study revealed that subcortical-cortical networks (striatal-frontal loop) in PwPD are compromised, leading to impaired comprehension of syntactically complex sentences.

Quality control is a critical step in the processing and analysis of functional magnetic resonance imaging data. Its purpose is to remove problematic data that could otherwise lead to downstream errors in the analysis and reporting of results. The manual inspection of data can be a laborious and error-prone process that is susceptible to human error. The development of automated tools aims to mitigate these issues. One such tool is pyfMRIqc, which we previously developed as a user-friendly method for assessing data quality. Yet, these methods still generate output that requires subjective interpretations about whether the quality of a given dataset meets an acceptable standard for further analysis. Here we present a quality control protocol using pyfMRIqc and assess the inter-rater reliability of four independent raters using this protocol for data from the fMRI Open QC project (https://osf.io/qaesm/). Data were classified by raters as either "include," "uncertain," or "exclude." There was moderate to substantial agreement between raters for "include" and "exclude," but little to no agreement for "uncertain." In most cases only a single rater used the "uncertain" classification for a given participant's data, with the remaining raters showing agreement for "include"/"exclude" decisions in all but one case. We suggest several approaches to increase rater agreement and reduce disagreement for "uncertain" cases, aiding classification consistency. [ABSTRACT FROM AUTHOR]

Resting-state (rs) fMRI has been widely used to examine brain-wide large-scale spatiotemporal architectures, known as resting-state networks (RSNs). Recent studies have focused on the temporally evolving characteristics of RSNs, but it is unclear what temporal characteristics are reflected in the networks. To address this issue, we devised a novel method for voxel-based visualization of spatiotemporal characteristics of rs-fMRI with a time scale of tens of seconds. We first extracted clusters of dominant activity-patterns using a region-of-interest approach and then used these temporal patterns of the clusters to obtain voxel-based activation patterns related to the clusters. We found that activation patterns related to the clusters temporally evolved with a characteristic temporal structure and showed mutual temporal alternations over minutes. The voxel-based representation allowed the decoding of activation patterns of the clusters in rs-fMRI using ameta-analysis of functional activations. The activation patterns of the clusters were correlated with behavioral measures. Taken together, our analysis highlights a novel approach to examine brain activity dynamics during rest. [ABSTRACT FROM AUTHOR]

Impaired emotion recognition is common in individuals with attention-deficit/hyperactivity disorder (ADHD) and may, via deficient emotion self-regulation, relate to the frequently co-occurring affective and social problems. The present study used an emotional face-matching task and functional magnetic resonance imaging (fMRI) to investigate neural responses during the processing of angry and fearful faces and visuo-spatial control stimuli. Additionally, measures for emotion dysregulation, ADHD type, and age were investigated in relation to the behavioral and neural fMRI data. We utilized a sample of 61 adolescents/young adults with ADHD and 51 age-matched healthy controls (age range: 12–28 years). Participants with ADHD had higher emotion dysregulation scores than controls. They also reacted slower and less accurate in response to emotional but not visuo-spatial control stimuli. Neural response differences between emotional and visuo-spatial trials were significantly smaller in cases, particularly in the left amygdala. While coupling between the right amygdala and bilateral ventromedial prefrontal cortex was stronger for emotional than visuo-spatial stimuli in control subjects, levels of positive coupling between the trial types did not significantly differ in participants with ADHD. Neither emotion dysregulation scores, nor ADHD type or age were related to the behavioral and neural processing alterations during the emotional face-matching task. Results indicate that emotion recognition deficits in ADHD are particularly associated with lower amygdala activation to emotional stimuli and alterations in the functional connections of the amygdala to medial prefrontal areas. Emotion recognition deficits and associated neural alterations were unrelated to emotion dysregulation, ADHD type, or age. [ABSTRACT FROM AUTHOR]

• Resting-state fMRI BOLD signal of the human brain is a superposition of EM and DM. • EM (essential mode) is asymmetric and the most dominant mode for individuals. • DM (DMN-like mode) is the second dominant mode that holds the default mode network. • Both EM and DM are activated with low-frequency oscillations in the below 0.2 Hz. • The changes in active regions of the brain during motor-task are reflected in EM. Resting-state of the human brain has been described by a combination of various basis modes including the default mode network (DMN) identified by fMRI BOLD signals in human brains. Whether DMN is the most dominant representation of the resting-state has been under question. Here, we investigated the unexplored yet fundamental nature of the resting-state. In the absence of global signal regression for the analysis of brain-wide spatial activity pattern, the fMRI BOLD spatiotemporal signals during the rest were completely decomposed into time-invariant spatial-expression basis modes (SEBMs) and their time-evolution basis modes (TEBMs). Contrary to our conventional concept above, similarity clustering analysis of the SEBMs from 166 human brains revealed that the most dominant SEBM cluster is an asymmetric mode where the distribution of the sign of the components is skewed in one direction, for which we call essential mode (EM), whereas the second dominant SEBM cluster resembles the spatial pattern of DMN. Having removed the strong 1/f noise in the power spectrum of TEBMs, the genuine oscillatory behavior embedded in TEBMs of EM and DMN-like mode was uncovered around the low-frequency range below 0.2 Hz. [ABSTRACT FROM AUTHOR]

Focused attention meditation (FAM) training has been shown to improve attention, but the neural basis of FAM on attention has not been thoroughly understood. Here, we aim to investigate the neural effect of a 2-month FAM training on novice meditators in a visual oddball task (a frequently adopted task to evaluate attention), evaluated with both ASL and BOLD fMRI. Using ASL, activation was increased in the middle cingulate (part of the salience network, SN) and temporoparietal (part of the frontoparietal network, FPN) regions; the FAM practice time was negatively associated with the longitudinal changes in activation in the medial prefrontal (part of the default mode network, DMN) and middle frontal (part of the FPN) regions. Using BOLD, the FAM practice time was positively associated with the longitudinal changes of activation in the inferior parietal (part of the dorsal attention network, DAN), dorsolateral prefrontal (part of the FPN), and precentral (part of the DAN) regions. The effect sizes for the activation changes and their association with practice time using ASL are significantly larger than those using BOLD. Our study suggests that FAM training may improve attention via modulation of the DMN, DAN, SN, and FPN, and ASL may be a sensitive tool to study the FAM effect on attention.

Quality control is a critical step in the processing and analysis of functional magnetic resonance imaging data. Its purpose is to remove problematic data that could otherwise lead to downstream errors in the analysis and reporting of results. The manual inspection of data can be a laborious and error-prone process that is susceptible to human error. The development of automated tools aims to mitigate these issues. One such tool is pyfMRIqc, which we previously developed as a user-friendly method for assessing data quality. Yet, these methods still generate output that requires subjective interpretations about whether the quality of a given dataset meets an acceptable standard for further analysis. Here we present a quality control protocol using pyfMRIqc and assess the inter-rater reliability of four independent raters using this protocol for data from the fMRI Open QC project (https://osf.io/qaesm/). Data were classified by raters as either “include,” “uncertain,” or “exclude.” There was moderate to substantial agreement between raters for “include” and “exclude,” but little to no agreement for “uncertain.” In most cases only a single rater used the “uncertain” classification for a given participant’s data, with the remaining raters showing agreement for “include”/“exclude” decisions in all but one case. We suggest several approaches to increase rater agreement and reduce disagreement for “uncertain” cases, aiding classification consistency.

Conventionally, cerebrovascular reactivity (CVR) is estimated as the amplitude of the hemodynamic response to vascular stimuli, most commonly carbon dioxide (CO2). While the CVR amplitude has established clinical utility, the temporal characteristics of CVR (dCVR) have been increasingly explored and may yield even more pathology-sensitive parameters. This work is motivated by the current need to evaluate the feasibility of dCVR modeling in various experimental conditions. In this work, we present a comparison of several recently published/utilized model-based deconvolution (response estimation) approaches for estimating the CO2 response function h(t), including maximum a posteriori likelihood (MAP), inverse logit (IL), canonical correlation analysis (CCA), and basis expansion (using Gamma and Laguerre basis sets). To aid the comparison, we devised a novel simulation framework that incorporates a wide range of SNRs, ranging from 10 to -7 dB, representative of both task and resting-state CO2 changes. In addition, we built ground-truth h(t) into our simulation framework, overcoming the conventional limitation that the true h(t) is unknown. Moreover, to best represent realistic noise found in fMRI scans, we extracted noise from in-vivo resting-state scans. Furthermore, we introduce a simple optimization of the CCA method (CCAopt) and compare its performance to these existing methods. Our findings suggest that model-based methods can accurately estimate dCVR even amidst high noise (i.e. resting-state), and in a manner that is largely independent of the underlying model assumptions for each method. We also provide a quantitative basis for making methodological choices, based on the desired dCVR parameters, the estimation accuracy and computation time. The BEL method provided the highest accuracy and robustness, followed by the CCAopt and IL methods. Of the three, the CCAopt method has the lowest computational requirements. These findings lay the foundation for wider adoption of dCVR estimation in CVR mapping.

Task-based functional MRI (fMRI) has greatly improved understanding of brain functioning, enabling the identification of brain areas associated with specific cognitive operations. Traditional analyses are limited to associating activation patterns in particular regions with specific cognitive operation, largely ignoring regional cross-talk or dynamic connectivity, which we propose is crucial for characterization of brain function in the context of task fMRI. We use connectotyping, which efficiently models functional brain connectivity to reveal the progression of temporal brain connectivity patterns in task fMRI. Connectotyping was employed on data from twenty-four participants (12 male, mean age 24.8 years, 2.57 std. dev) who performed a widely spaced event-related fMRI word vs. pseudoword decision task, where stimuli were presented every 20 s. After filtering for movement, we ended up with 15 participants that completed each trial and had enough usable data for our analyses. Connectivity matrices were calculated per participant across time for each stimuli type. A Repeated Measures ANOVA applied on the connectotypes was used to characterize differences across time for words and pseudowords. Our group level analyses found significantly different dynamic connectivity patterns during word vs. pseudoword processing between the Fronto-Parietal and Cingulo-Parietal Systems, areas involved in cognitive task control, memory retrieval, and semantic processing. Our findings support the presence of dynamic changes in functional connectivity during task execution and that such changes can be characterized using connectotyping but not with traditional Pearson's correlations. [ABSTRACT FROM AUTHOR]

Alcohol approach bias, a tendency to approach rather than to avoid alcohol and alcohol-related cues regardless of associated negative consequences, is an emerging key characteristic of alcohol use disorder (AUD). Reaction times from the Approach-Avoidance Task (AAT) can be used to quantify alcohol approach bias. However, only a handful of studies have investigated the neural correlates of implicit alcohol approach behavior. Graph Theory Analysis (GTA) metrics, specifically, weighted global efficiency (wGE), community detection, and inter-community information integration were used to analyze functional magnetic resonance imaging (fMRI) data of an in-scanner version of the AAT from 31 heavy drinking Veterans with AUD (HDV) engaged in out-patient treatment and 19 healthy Veterans as controls (HC). We found a functional imprint of alcohol approach bias in HDVs. HDVs showed significantly higher wGE values for approaching than for avoiding alcohol, indicating that their brain was more efficiently organized or functionally set to approach alcohol in the presence to alcohol-related external cues. In contrast, Brains of HCs did not show such a processing advantage for either the approach or avoid condition. Further post-hoc analyses revealed that HDVs and HCs differed in how they implemented top-down control when approaching/avoiding alcohol and in how the fronto-parietal control network interacted with subsystems of the default mode network. These findings contribute to understanding the complex neural underpinnings of alcohol approach bias and lay the foundation for developing more potent and targeted interventions to modify these neural patterns in AUD patients., Competing Interests: Conflict of Interest The authors declare no conflict of interest.

Task-based functional MRI (fMRI) has greatly improved understanding of brain functioning, enabling the identification of brain areas associated with specific cognitive operations. Traditional analyses are limited to associating activation patterns in particular regions with specific cognitive operation, largely ignoring regional cross-talk or dynamic connectivity, which we propose is crucial for characterization of brain function in the context of task fMRI. We use connectotyping, which efficiently models functional brain connectivity to reveal the progression of temporal brain connectivity patterns in task fMRI. Connectotyping was employed on data from twenty-four participants (12 male, mean age 24.8 years, 2.57 std. dev) who performed a widely spaced event-related fMRI word vs. pseudoword decision task, where stimuli were presented every 20 s. After filtering for movement, we ended up with 15 participants that completed each trial and had enough usable data for our analyses. Connectivity matrices were calculated per participant across time for each stimuli type. A Repeated Measures ANOVA applied on the connectotypes was used to characterize differences across time for words and pseudowords. Our group level analyses found significantly different dynamic connectivity patterns during word vs. pseudoword processing between the Fronto-Parietal and Cingulo-Parietal Systems, areas involved in cognitive task control, memory retrieval, and semantic processing. Our findings support the presence of dynamic changes in functional connectivity during task execution and that such changes can be characterized using connectotyping but not with traditional Pearson’s correlations.

Task fMRI provides an opportunity to analyze the working mechanisms of the human brain during specific experimental paradigms. Deep learning models have increasingly been applied for decoding and encoding purposes study to representations in task fMRI data. More recently, graph neural networks, or neural networks models designed to leverage the properties of graph representations, have recently shown promise in task fMRI decoding studies. Here, we propose an end-to-end graph convolutional network (GCN) framework with three convolutional layers to classify task fMRI data from the Human Connectome Project dataset. We compared the predictive performance of our GCN model across four of the most widely used node embedding algorithms—NetMF, RandNE, Node2Vec, and Walklets—to automatically extract the structural properties of the nodes in the functional graph. The empirical results indicated that our GCN framework accurately predicted individual differences (0.978 and 0.976) with the NetMF and RandNE embedding methods, respectively. Furthermore, to assess the effects of individual differences, we tested the classification performance of the model on sub-datasets divided according to gender and fluid intelligence. Experimental results indicated significant differences in the classification predictions of gender, but not high/low fluid intelligence fMRI data. Our experiments yielded promising results and demonstrated the superior ability of our GCN in modeling task fMRI data. [ABSTRACT FROM AUTHOR]

Background: The anterior insular cortex (AIC), a prominent salience network node, integrates interoceptive information and emotional states into decision making. While AIC activation during delay discounting (DD) in alcohol use disorder (AUD) has been previously reported, the associations between AIC activation, impulsive choice, alcohol consumption, and connectivity remain unknown. We therefore tested AIC brain responses during DD in heavy drinkers and their association with DD performance, alcohol drinking, and task‐based connectivity. Methods: Twenty‐nine heavy drinkers (12 females; mean (SD) age=31.5 ± 6.1 years; mean (SD)=40.8 ± 23.4 drinks/week) completed a DD task during functional MRI. Regions activated during DD decision making were tested for correlation with DD behavior and alcohol drinking. Psychophysiological interaction (PPI) models assessed the task‐dependent functional connectivity (FC) of activation during choice. Results: Delay discounting choice activated bilateral anterior insular cortex, anterior cingulate cortex, and left precentral gyrus. Right dorsal (d) AIC activation during choice negatively correlated withdiscounting of delayed rewards and alcohol consumption. PPI analysis revealed FC of the right dAIC to both the anterior and posterior cingulate cortices—key nodes in the midline default mode network. Conclusions: Greater dAIC involvement in intertemporal choice may confer more adaptive behavior (lower impulsivity and alcohol consumption). Moreover, salience network processes governing discounting may require midline default mode (precuneus/posterior cingulate cortex) recruitment. These findings supporta key adaptive role for right dAIC in decision making involving future rewards and risky drinking. [ABSTRACT FROM AUTHOR]

Background: Pathological brain processes may induce adaptive cortical reorganization, however, the mechanisms underlying neuroplasticity that occurs in the presence of lesions in eloquent areas are not fully explained. The aim of this study was to evaluate functional compensatory cortical activations in patients with frontal brain gliomas during a phonemic fluency task and to explore correlations with cognitive performance, white matter tracts microstructural alterations, and tumor histopathological and molecular characterization. Methods: Fifteen patients with frontal glioma were preoperatively investigated with an MRI study on a 3T scanner and a subgroup underwent an extensive neuropsychological assessment. The hemispheric laterality index (LI) was calculated through phonemic fluency task functional MRI (fMRI) activations in the frontal, parietal, and temporal lobe parcellations. Diffusion-weighted images were acquired for all patients and for a group of 24 matched healthy volunteers. Arcuate Fasciculus (AF) and Frontal Aslant Tract (FAT) tractography was performed using constrained spherical deconvolution diffusivity modeling and probabilistic fiber tracking. All patients were operated on with a resective aim and underwent adjuvant therapies, depending on the final diagnosis. Results: All patients during the phonemic fluency task fMRI showed left hemispheric dominance in temporal and parietal regions. Regarding frontal regions (i.e., frontal operculum) we found right hemispheric dominance that increases when considering only those patients with tumors located on the left side. These latter activations positively correlate with verbal and visuo-spatial short-term memory, and executive functions. No correlations were found between the left frontal operculum and cognitive performance. Furthermore, patients with IDH-1 mutation and without TERT mutation, showed higher rightward frontal operculum fMRI activations and better cognitive performance in tests measuring general cognitive abilities, semantic fluency, verbal short-term memory, and executive functions. As for white matter tracts, we found left and right AF and FAT microstructural alterations in patients with, respectively, left-sided and right-side glioma compared to controls. Conclusions: Compensatory cortical activation of the corresponding region in the non-dominant hemisphere and its association with better cognitive performance and more favorable histopathological and molecular tumor characteristics shed light on the neuroplasticity mechanisms that occur in the presence of a tumor, helping to predict the rate of post-operative deficit, with the final goal of improving patients'quality of life. [ABSTRACT FROM AUTHOR]

Abstract Background As it has been recorded in ancient Chinese classics, Yanglingquan (GB34) and Dannangxue (EX-LE6) are two important acupoints that can regulate the function of the gallbladder. Acupuncture at these two acupoints is considered particularly effective for gallbladder disease treatment, especially for alleviating gallbladder stone disease (GSD) symptoms that can be aggravated after intaking high-fat food. However, the superior effect between the two acupoints still needs to be further explored, as well as the underlying central mechanism has never been investigated to date. Methods and design Ninety participants diagnosed with GSD will be randomly divided into group A (acupuncture at GB34), group B (acupuncture at EX-LE6), and group C (acupuncture at non-acupoint) in a ratio of 1:1:1. All of them will receive a 30-min acupuncture treatment with fatty-food cues being presented before and after acupuncture. During the task, participants will be scanned by MRI and required to rate their desire for high-/low-fat food with an 11-point Likert scale. Additionally, the participants’ pain/discomfort sensation will be evaluated using the Numeric Rating Scale (NRS) at four timepoints, including before the 1st task fMRI scan, before and after acupuncture, and after the 2nd task fMRI scan. For both behavior and fMRI data, the ANOVA analysis will be conducted among three groups to testify the immediate effect of GB34 and EX-LE6. The post hoc t-test will be employed to further explore the superiority between acupuncture with GB34 and EX-LE6. Furthermore, correlation analyses will be conducted to investigate a possible correlation between neural changes and clinical data. Discussion In comparison to the non-acupoint, the results will firstly explore the superior effect between acupuncture with GB34 and EX-LE6 on GSD patients by observing their behavioral and neural response change to fatty-food cue, and then to investigate the underlying central mechanism. Trial registration Chinese Clinical Trial Registry ChiCTR2000034368 . Registered on 3 July 2020.

BackgroundPathological brain processes may induce adaptive cortical reorganization, however, the mechanisms underlying neuroplasticity that occurs in the presence of lesions in eloquent areas are not fully explained. The aim of this study was to evaluate functional compensatory cortical activations in patients with frontal brain gliomas during a phonemic fluency task and to explore correlations with cognitive performance, white matter tracts microstructural alterations, and tumor histopathological and molecular characterization.MethodsFifteen patients with frontal glioma were preoperatively investigated with an MRI study on a 3T scanner and a subgroup underwent an extensive neuropsychological assessment. The hemispheric laterality index (LI) was calculated through phonemic fluency task functional MRI (fMRI) activations in the frontal, parietal, and temporal lobe parcellations. Diffusion-weighted images were acquired for all patients and for a group of 24 matched healthy volunteers. Arcuate Fasciculus (AF) and Frontal Aslant Tract (FAT) tractography was performed using constrained spherical deconvolution diffusivity modeling and probabilistic fiber tracking. All patients were operated on with a resective aim and underwent adjuvant therapies, depending on the final diagnosis.ResultsAll patients during the phonemic fluency task fMRI showed left hemispheric dominance in temporal and parietal regions. Regarding frontal regions (i.e., frontal operculum) we found right hemispheric dominance that increases when considering only those patients with tumors located on the left side. These latter activations positively correlate with verbal and visuo-spatial short-term memory, and executive functions. No correlations were found between the left frontal operculum and cognitive performance. Furthermore, patients with IDH-1 mutation and without TERT mutation, showed higher rightward frontal operculum fMRI activations and better cognitive performance in tests measuring general cognitive abilities, semantic fluency, verbal short-term memory, and executive functions. As for white matter tracts, we found left and right AF and FAT microstructural alterations in patients with, respectively, left-sided and right-side glioma compared to controls.ConclusionsCompensatory cortical activation of the corresponding region in the non-dominant hemisphere and its association with better cognitive performance and more favorable histopathological and molecular tumor characteristics shed light on the neuroplasticity mechanisms that occur in the presence of a tumor, helping to predict the rate of post-operative deficit, with the final goal of improving patients'quality of life.

Understanding the organizational principles of human brain activity at the systems level remains a major challenge in network neuroscience. Here, we introduce a fully data-driven approach based on graph learning to extract meaningful repeating network patterns from regionally-averaged timecourses. We use the Graph Laplacian Mixture Model (GLMM), a generative model that treats functional data as a collection of signals expressed on multiple underlying graphs. By exploiting covariance between activity of brain regions, these graphs can be learned without resorting to structural information. To validate the proposed technique, we first apply it to task fMRI with a known experimental paradigm. The probability of each graph to occur at each time-point is found to be consistent with the task timing, while the spatial patterns associated to each epoch of the task are in line with previously established activation patterns using classical regression analysis. We further on apply the technique to resting state data, which leads to extracted graphs that correspond to well-known brain functional activation patterns. The GLMM allows to learn graphs entirely from the functional activity that, in practice, turn out to reveal high degrees of similarity to the structural connectome. The Default Mode Network (DMN) is always captured by the algorithm in the different tasks and resting state data. Therefore, we compare the states corresponding to this network within themselves and with structure.Overall, this method allows us to infer relevant functional brain networks without the need of structural connectome information. Moreover, we overcome the limitations of windowing the time sequences by feeding the GLMM with the whole functional signal and neglecting the focus on sub-portions of the signals.

The general linear model (GLM) is a widely popular and convenient tool for estimating the functional brain response and identifying areas of significant activation during a task or stimulus. However, the classical GLM is based on a massive univariate approach that does not explicitly leverage the similarity of activation patterns among neighboring brain locations. As a result, it tends to produce noisy estimates and be underpowered to detect significant activations, particularly in individual subjects and small groups. A recently proposed alternative, a cortical surface-based spatial Bayesian GLM, leverages spatial dependencies among neighboring cortical vertices to produce more accurate estimates and areas of functional activation. The spatial Bayesian GLM can be applied to individual and group-level analysis. In this study, we assess the reliability and power of individual and group-average measures of task activation produced via the surface-based spatial Bayesian GLM. We analyze motor task data from 45 subjects in the Human Connectome Project (HCP) and HCP Retest datasets. We also extend the model to multi-run analysis and employ subject-specific cortical surfaces rather than surfaces inflated to a sphere for more accurate distance-based modeling. Results show that the surface-based spatial Bayesian GLM produces highly reliable activations in individual subjects and is powerful enough to detect trait-like functional topologies. Additionally, spatial Bayesian modeling enhances reliability of group-level analysis even in moderately sized samples (n=45). Notably, the power of the spatial Bayesian GLM to detect activations above a scientifically meaningful effect size is nearly invariant to sample size, exhibiting high power even in small samples (n=10). The spatial Bayesian GLM is computationally efficient in individuals and groups and is convenient to implement with the open-source BayesfMRI R package.

Relating individual differences in cognitive traits to brain functional organization is a long-lasting challenge for the neuroscience community. Individual intelligence scores were previously predicted from whole-brain connectivity patterns, extracted from functional magnetic resonance imaging (fMRI) data acquired at rest. Recently, it was shown that task-induced brain activation maps outperform these resting-state connectivity patterns in predicting individual intelligence, suggesting that a cognitively demanding environment improves prediction of cognitive abilities. Here, we use data from the Human Connectome Project to predict task-induced brain activation maps from resting-state fMRI, and proceed to use these predicted activity maps to further predict individual differences in a variety of traits. While models based on original task activation maps remain the most accurate, models based on predicted maps significantly outperformed those based on the resting-state connectome. Thus, we provide a promising approach for the evaluation of measures of human behavior from brain activation maps, that could be used without having participants actually perform the tasks.

We propose a numerical-based approach extending the conditional MVAR Granger causality (MVGC) analysis for the construction of directed connectivity networks in the presence of both exogenous/stimuli and modulatory inputs. The performance of the proposed scheme is validated using both synthetic stochastic data considering also the influence of haemodynamics latencies and a benchmark fMRI dataset related to the role of attention in the perception of visual motion. Theparticular fMRI dataset has been used in many studies to evaluate alternative model hypotheses using the Dynamic Causal Modelling (DCM) approach. Based on the use of the Bayes factor, we show that the obtained GC connectivity network compares well to a reference model that has been selected through DCM analysis among other candidate models. Thus, our findings suggest that the proposed scheme can be successfully used as a stand-alone or complementary to DCM approach to find directed causal connectivity patterns in task-related fMRI studies.

Task-based functional magnetic resonance imaging (tfMRI) is a widely used neuroimaging technique in exploring brain networks and functions associated with cognitive behaviors. Traditionally, the general linear model (GLM) is the most popular method in tfMRI data analysis due to its simpleness and robustness. This model-driven method adopts a canonical hemodynamic response function (HRF) and its various derivatives to construct regressors in the design matrix and estimate changes in the tfMRI data. However, a possible limitation of current model-driven methods is that the HRF is fixed and non-adaptive which may overlook other diverse and concurrent brain networks. In order to overcome these limitations, we proposed a novel hybrid framework, supervised brain network learning based on deep recurrent neural networks (SUDRNN), to reconstruct the diverse and concurrent functional brain networks. Specifically, this hybrid framework first takes advantage of the great capacity of deep recurrent neural networks (DRNN) in modeling sequential data to learn the diverse regressors from real tfMRI data. After that, it utilizes the effective supervised dictionary learning (SDL) method to reconstruct both the task-related functional brain networks and other latent brain networks simultaneously. Extensive experiment results on different tfMRI datasets from Human connectome project (HCP) demonstrated the superiority of the proposed framework.

In the field of neuroimaging and cognitive neuroscience, functional Magnetic Resonance Imaging (fMRI) has been widely used to study the functional localization and connectivity of the brain. However, the inherently low signal-to-noise ratio (SNR) of the fMRI signals greatly limits the accuracy and resolution of current studies. In addressing this fundamental challenge in fMRI analytics, in this work we develop and implement a denoising method for task fMRI (tfMRI) data in order to delineate the high-resolution spatial pattern of the brain activation and functional connectivity via dictionary learning and sparse coding (DLSC). In addition to the traditional unsupervised dictionary learning model which has shown success in image denoising, we further utilize the prior knowledge of task paradigm to learn a dictionary consisting of both data-driven and model-driven terms for a more stable sparse representation of the data. The proposed method is applied to preprocess the motor tfMRI dataset from Human Connectome Project (HCP) for the purpose of brain activation detection and functional connectivity estimation. Comparison between the results from original and denoised fMRI data shows that the disruptive brain activation and functional connectivity patterns can be recovered, and the prominence of such patterns is improved through denoising. The proposed method is then compared with the temporal non-local means (tNLM)-based denoising method and shows consistently superior performance in various experimental settings. The promising results show that the proposed DLSC-based fMRI denoising method can effectively reduce the noise level of the fMRI signals and increase the interpretability of the inferred results, therefore constituting a crucial part of the preprocessing pipeline and provide the foundation for further high-resolution functional analysis.

Brain responses recorded during fMRI are thought to reflect both rapid, stimulus-evoked activity and the propagation of spontaneous activity through brain networks. In the current work, we describe a method to improve the estimation of task-evoked brain activity by first “filtering-out the intrinsic propagation of pre-event activity from the BOLD signal. We do so using Mesoscale Individualized NeuroDynamic (MINDy; Singh et al. 2020b) models built from individualized resting-state data to subtract the propagation of spontaneous activity from the task-fMRI signal (MINDy-based Filtering). After filtering, time-series are analyzed using conventional techniques. Results demonstrate that this simple operation significantly improves the statistical power and temporal precision of estimated group-level effects. Moreover, use of MINDy-based filtering increased the similarity of neural activation profiles and prediction accuracy of individual differences in behavior across tasks measuring the same construct (cognitive control). Thus, by subtracting the propagation of previous activity, we obtain better estimates of task-related neural effects.

Multiple sclerosis (MS) is a neurological disorder affecting the central nervous system and features extensive functional brain changes that are poorly understood but relate strongly to clinical impairments. Functional magnetic resonance imaging (fMRI) is a non-invasive, powerful technique able to map activity of brain regions and to assess how such regions interact for an efficient brain network. FMRI has been widely applied to study functional brain changes in MS, allowing to investigate functional plasticity consequent to disease-related structural injury. The first studies in MS using active fMRI tasks mainly aimed to study such plastic changes by identifying abnormal activity in salient brain regions (or systems) involved by the task. In later studies the focus shifted towards resting state (RS) functional connectivity (FC) studies, which aimed to map large-scale functional networks of the brain and to establish how MS pathology impairs functional integration, eventually leading to the hypothesized network collapse as patients clinically progress. This review provides a summary of the main findings from studies using task-based and RS fMRI and illustrates how functional brain alterations relate to clinical disability and cognitive deficits in this condition. We also give an overview of longitudinal studies that used task-based and RS fMRI to monitor disease evolution and effects of motor and cognitive rehabilitation. In addition, we discuss the results of studies using newer technologies involving time-varying FC to investigate abnormal dynamism and flexibility of network configurations in MS. Finally, we show some preliminary results from two recent topics (i.e., multimodal MRI analysis and artificial intelligence) that are receiving increasing attention. Together, these functional studies could provide new (conceptual) insights into disease stage-specific mechanisms underlying progression in MS, with recommendations for future research.

Introduction: Functional magnetic resonance imaging (fMRI) has become a fundamental tool for studying brain function. However, the presence of serial correlations in fMRI data complicates data analysis, violates the statistical assumptions of analyses methods, and can lead to incorrect conclusions in fMRI studies., Methods: In this paper, we show that conventional whitening procedures designed for data with longer repetition times (TRs) (>2 s) are inadequate for the increasing use of short-TR fMRI data. Furthermore, we comprehensively investigate the shortcomings of existing whitening methods and introduce an iterative whitening approach named "IDAR" (Iterative Data-adaptive Autoregressive model) to address these shortcomings. IDAR employs high-order autoregressive (AR) models with flexible and data-driven orders, offering the capability to model complex serial correlation structures in both short-TR and long-TR fMRI datasets., Results: Conventional whitening methods, such as AR(1), ARMA(1,1), and higher-order AR, were effective in reducing serial correlation in long-TR data but were largely ineffective in even reducing serial correlation in short-TR data. In contrast, IDAR significantly outperformed conventional methods in addressing serial correlation, power, and Type-I error for both long-TR and especially short-TR data. However, IDAR could not simultaneously address residual correlations and inflated Type-I error effectively., Discussion: This study highlights the urgent need to address the problem of serial correlation in short-TR (< 1 s) fMRI data, which are increasingly used in the field. Although IDAR can address this issue for a wide range of applications and datasets, the complexity of short-TR data necessitates continued exploration and innovative approaches. These efforts are essential to simultaneously reduce serial correlations and control Type-I error rates without compromising analytical power., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Yue, Webster, Grabowski, Shojaie and Jahanian.)

Stroke-related tissue damage within lesioned brain areas is topologically non-uniform and has underlying tissue composition changes that may have important implications for rehabilitation. However, we know of no uniformly accepted, objective non-invasive methodology to identify pericavitational areas within the chronic stroke lesion. To fill this gap, we propose a novel magnetic resonance imaging (MRI) methodology to objectively quantify the lesion core and surrounding pericavitational perimeter, which we call tissue integrity gradation via T2w T1w ratio (TIGR). TIGR uses standard T1-weighted (T1w) and T2-weighted (T2w) anatomical images routinely collected in the clinical setting. TIGR maps are analyzed with relation to subject-specific gray matter and cerebrospinal fluid thresholds and binned to create a false colormap of tissue damage within the stroke lesion, and these are further categorized into low-, medium-, and high-damage areas. We validate TIGR by showing that the cerebral blood flow within the lesion reduces with greater tissue damage (p = 0.005). We further show that a significant task activity can be detected in pericavitational areas and that medium-damage areas contain a significantly lower magnitude of hemodynamic response function than the adjacent damaged areas (p < 0.0001). We also demonstrate the feasibility of using TIGR maps to extract multivariate brain–behavior relationships (p < 0.05) and show general agreement in location compared to binary lesion, T1w-only, and T2w-only maps but that the extent of brain behavior maps may depend on signal sensitivity as denoted by the sparseness coefficient (p < 0.0001). Finally, we show the feasibility of quantifying TIGR in early and late subacute stroke phases, where higher-damage areas were smaller in size (p = 0.002) and that lesioned voxels transition from lower to higher damage with increasing time post-stroke (p = 0.004). We conclude that TIGR is able to (1) identify tissue damage gradient within the stroke lesion across different post-stroke timepoints and (2) more objectively delineate lesion core from pericavitational areas wherein such areas demonstrate reasonable and expected physiological and functional impairments. Importantly, because T1w and T2w scans are routinely collected in the clinic, TIGR maps can be readily incorporated in clinical settings without additional imaging costs or patient burden to facilitate decision processes related to rehabilitation planning.

Stroke-related tissue damage within lesioned brain areas is topologically non-uniform and has underlying tissue composition changes that may have important implications for rehabilitation. However, we know of no uniformly accepted, objective non-invasive methodology to identify pericavitational areas within the chronic stroke lesion. To fill this gap, we propose a novel magnetic resonance imaging (MRI) methodology to objectively quantify the lesion core and surrounding pericavitational perimeter, which we call tissue integrity gradation via T2w T1w ratio (TIGR). TIGR uses standard T1-weighted (T1w) and T2-weighted (T2w) anatomical images routinely collected in the clinical setting. TIGR maps are analyzed with relation to subject-specific gray matter and cerebrospinal fluid thresholds and binned to create a false colormap of tissue damage within the stroke lesion, and these are further categorized into low-, medium-, and high-damage areas. We validate TIGR by showing that the cerebral blood flow within the lesion reduces with greater tissue damage (p = 0.005). We further show that a significant task activity can be detected in pericavitational areas and that medium-damage areas contain a significantly lower magnitude of hemodynamic response function than the adjacent damaged areas (p < 0.0001). We also demonstrate the feasibility of using TIGR maps to extract multivariate brain–behavior relationships (p < 0.05) and show general agreement in location compared to binary lesion, T1w-only, and T2w-only maps but that the extent of brain behavior maps may depend on signal sensitivity as denoted by the sparseness coefficient (p < 0.0001). Finally, we show the feasibility of quantifying TIGR in early and late subacute stroke phases, where higher-damage areas were smaller in size (p = 0.002) and that lesioned voxels transition from lower to higher damage with increasing time post-stroke (p = 0.004). We conclude that TIGR is able to (1) identify tissue damage gradient within the stroke lesion across different post-stroke timepoints and (2) more objectively delineate lesion core from pericavitational areas wherein such areas demonstrate reasonable and expected physiological and functional impairments. Importantly, because T1w and T2w scans are routinely collected in the clinic, TIGR maps can be readily incorporated in clinical settings without additional imaging costs or patient burden to facilitate decision processes related to rehabilitation planning. [ABSTRACT FROM AUTHOR]