Search

Background: Numerous studies show that electroconvulsive therapy (ECT) induces hippocampal neuroplasticity, but findings are inconsistent regarding its clinical relevance. This study aims to investigate ECT-induced plasticity of anterior and posterior hippocampi using mathematical complexity measures in neuroimaging, namely Higuchi's fractal dimension (HFD) for fMRI time series and the fractal dimension of cortical morphology (FD-CM). Furthermore, we explore the potential of these complexity measures to predict ECT treatment response. Methods: Twenty patients with a current depressive episode (16 with major depressive disorder and 4 with bipolar disorder) underwent MRI-scans before and after an ECT-series. Twenty healthy controls matched for age and sex were also scanned twice for comparison purposes. Resting-state fMRI data were processed, and HFD was computed for anterior and posterior hippocampi. Group-by-time effects for HFD in anterior and posterior hippocampi were calculated and correlations between HFD changes and improvement in depression severity were examined. For FD-CM analyses, we preprocessed structural MRI with CAT12′s surface-based methods. We explored group-by-time effects for FD-CM and the predictive value of baseline HFD and FD-CM for treatment outcome. Results: Patients exhibited a significant increase in bilateral hippocampal HFD from baseline to follow-up scans. Right anterior hippocampal HFD increase was associated with reductions in depression severity. We found no group differences and group-by-time effects in FD-CM. After applying a whole-brain regression analysis, we found that baseline FD-CM in the left temporal pole predicted reduction of overall depression severity after ECT. Baseline hippocampal HFD did not predict treatment outcome. Conclusion: This study suggests that HFD and FD-CM are promising imaging markers to investigate ECT-induced neuroplasticity associated with treatment response.

Transcranial alternating current stimulation (tACS) is an efficient neuromodulation technique that enhances cognitive function in a non-invasive manner. Using functional magnetic resonance imaging, we investigated whether tACS with different phase lags (0° and 180°) between the dorsal anterior cingulate and left dorsolateral prefrontal cortices modulated inhibitory control performance during the Stroop task. We found out-of-phase tACS mediated improvements in task performance, which was neurodynamically reflected as putamen, dorsolateral prefrontal, and primary motor cortical activation as well as prefrontal-based top-down functional connectivity. Our observations uncover the neurophysiological bases of tACS-phase-dependent neuromodulation and provide a feasible non-invasive approach to effectively modulate inhibitory control.

Aberrant susceptibility due to iron level abnormality and brain network disconnections are observed in Alzheimer's disease (AD), with disrupted iron homeostasis hypothesized to be linked to AD pathology and neuronal loss. However, whether associations exist between abnormal quantitative susceptibility mapping (QSM), brain atrophy, and altered brain connectome in AD remains unclear. Based on multi-parametric brain imaging data from 30 AD patients and 26 healthy controls enrolled at the China-Japan Friendship Hospital, we investigated the abnormality of the QSM signal and volumetric measure across 246 brain regions in AD patients. The structural and functional connectomes were constructed based on diffusion MRI tractography and functional connectivity, respectively. The network topology was quantified using graph theory analyses. We identified seven brain regions with both reduced cortical thickness and abnormal QSM (p < 0.05) in AD, including the right superior frontal gyrus, left superior temporal gyrus, right fusiform gyrus, left superior parietal lobule, right superior parietal lobule, left inferior parietal lobule, and left precuneus. Correlations between cortical thickness and network topology computed across patients in the AD group resulted in statistically significant correlations in five of these regions, with higher correlations in functional compared to structural topology. We computed the correlation between network topological metrics, QSM value and cortical thickness across regions at both individual and group-averaged levels, resulting in a measure we call spatial correlations. We found a decrease in the spatial correlation of QSM and the global efficiency of the structural network in AD patients at the individual level. These findings may provide insights into the complex relationships among QSM, brain atrophy, and brain connectome in AD.

Functional neuroimaging methods like fMRI and PET are vital in neuroscience research, but require that subjects remain still throughout the scan. In animal research, anesthetic agents are typically applied to facilitate the acquisition of high-quality data with minimal motion artifact. However, anesthesia can have profound effects on brain metabolism, selectively altering dynamic neural networks and confounding the acquired data. To overcome the challenge, we have developed a novel head fixation device designed to support awake rat brain imaging. A validation experiment demonstrated that the device effectively minimizes animal motion throughout the scan, with mean absolute displacement and mean relative displacement of 0.0256 (SD: 0.001) and 0.009 (SD: 0.002), across eight evaluated subjects throughout fMRI image acquisition (total scanning time per subject: 31 min, 12 s). Furthermore, the awake scans did not induce discernable stress to the animals, with stable physiological parameters throughout the scan (Mean HR: 344, Mean RR: 56, Mean SpO2: 94 %) and unaltered serum corticosterone levels (p = 0.159). In conclusion, the device presented in this paper offers an effective and safe method of acquiring functional brain images in rats, allowing researchers to minimize the confounding effects of anesthetic use.

Giant serpentine aneurysms are rare huge and partially thrombosed aneurysms, with an eccentric tortuous intra-aneurysmal vascular channel. Surgical treatment is often necessary due to the great mass effect. We describe a case of a left-handed woman with a giant serpentine aneurysm of the left middle cerebral artery whose management was complex. The challenge was to exclude the aneurysm from circulation, reduce the mass effect, and, mostly, preserve the language function. Since the patient was left-handed the language dominance needed to be assessed; functional MRI (fMRI) and Wada test (WT) showed a right dominance. Surgical treatment was performed, as a complication, the patient developed left fronto-basal ischemia with a slight paresis of the right hand but without any language deficit. Our case shows the importance of a multidisciplinary team in patient management, with a pivotal role of neuroradiological functional tests in presurgical planning.

Background: Aphasia affects approximately one-third of stroke patients and yet its rehabilitation outcomes are often unsatisfactory. More effective strategies are needed to promote recovery. Objective: We aimed to examine the efficacy and safety of the theta-burst stimulation (TBS) on the language area in the superior frontal gyrus (SFG) localized by personalized functional imaging, in facilitating post-stroke aphasia recovery. Methods: This randomized sham-controlled trial uses a parallel design (intermittent TBS [iTBS] in ipsilesional hemisphere vs. continuous TBS [cTBS] in contralesional hemisphere vs. sham group). Participants had aphasia symptoms resulting from their first stroke in the left hemisphere at least one month prior. Participants received three-week speech-language therapy coupled with either active or sham stimulation applied to the left or right SFG. The primary outcome was the change in Western Aphasia Battery-Revised (WAB-R) aphasia quotient after the three-week treatment. The secondary outcome was WAB-R aphasia quotient improvement after one week of treatment. Results: Ninety-seven patients were screened between January 2021 and January 2022, 45 of whom were randomized and 44 received intervention (15 in each active group, 14 in sham). Both iTBS (estimated difference = 14.75, p

Background: Phenylketonuria (PKU) is a rare inborn error of metabolism characterized by impaired catabolism of the amino acid phenylalanine (Phe) into tyrosine. Cross-sectional studies suggest slight alterations in cognitive performance and neural activation in adults with early-treated PKU. The influence of high Phe levels on brain function in adulthood, however, remains insufficiently studied. Therefore, we aimed to explore the effect of a four-week period of oral Phe administration − simulating a controlled discontinuation of Phe restriction and raising Phe to an off-diet scenario − on working memory-related neural activation and cerebral blood flow (CBF). Methods: We conducted a randomized, placebo-controlled, double-blind, crossover, non-inferiority trial to assess the effect of a high Phe load on working memory-related neural activation and CBF in early-treated adults with classical PKU. Twenty-seven patients with early-treated classical PKU were included and underwent functional magnetic resonance imaging (fMRI) of the working memory network and arterial spin labeling (ASL) MRI to assess CBF before and after a four-week intervention with Phe and placebo. At each of the four study visits, fMRI working memory task performance (reaction time and accuracy) and plasma Phe, tyrosine, and tryptophan levels were obtained. Additionally, cerebral Phe was determined by 1H-MR spectroscopy. Results: Plasma Phe and cerebral Phe were significantly increased after the Phe intervention. However, no significant effect of Phe compared to placebo was found on neural activation and CBF. Regarding fMRI task performance, a significant impact of the Phe intervention on 1-back reaction time was observed with slower reaction times following the Phe intervention, whereas 3-back reaction time and accuracy did not differ following the Phe intervention compared to the placebo intervention. Conclusion: Results from this present trial simulating a four-week discontinuation of the Phe-restricted diet showed that a high Phe load did not uniformly affect neural markers and cognition in a statistically significant manner. These results further contribute to the discussion on safe Phe levels during adulthood and suggest that a four-week discontinuation of Phe-restricted diet does not demonstrate significant changes in brain function.

Older adults with impairment in contrast sensitivity (CS), the ability to visually perceive differences in light and dark, are more likely to demonstrate limitations in mobility function, but the mechanisms underlying this relationship are poorly understood. We sought to determine if functional brain networks important to visual processing and mobility may help elucidate possible neural correlates of this relationship. This cross-sectional analysis utilized functional MRI both at rest and during a motor imagery (MI) task in 192 community-dwelling, cognitively-unimpaired older adults ≥ 70 years of age from the Brain Networks and Mobility study (B-NET). Brain networks were partitioned into network communities, groups of regions that are more interconnected with each other than the rest of the brain, the spatial consistency of the communities for multiple brain subnetworks was assessed. Lower baseline binocular CS was significantly associated with degraded sensorimotor network (SMN) community structure at rest. During the MI task, lower binocular CS was significantly associated with degraded community structure in both the visual (VN) and default mode network (DMN). These findings may suggest shared neural pathways for visual and mobility dysfunction that could be targeted in future studies.

Resting state functional magnetic resonance imaging (rsfMRI) provides researchers and clinicians with a powerful tool to examine functional connectivity across large-scale brain networks, with ever-increasing applications to the study of neurological disorders, such as traumatic brain injury (TBI). While rsfMRI holds unparalleled promise in systems neurosciences, its acquisition and analytical methodology across research groups is variable, resulting in a literature that is challenging to integrate and interpret. The focus of this narrative review is to address the primary methodological issues including investigator decision points in the application of rsfMRI to study the consequences of TBI. As part of the ENIGMA Brain Injury working group, we have collaborated to identify a minimum set of recommendations that are designed to produce results that are reliable, harmonizable, and reproducible for the TBI imaging research community. Part one of this review provides the results of a literature search of current rsfMRI studies of TBI, highlighting key design considerations and data processing pipelines. Part two outlines seven data acquisition, processing, and analysis recommendations with the goal of maximizing study reliability and between-site comparability, while preserving investigator autonomy. Part three summarizes new directions and opportunities for future rsfMRI studies in TBI patients. The goal is to galvanize the TBI community to gain consensus for a set of rigorous and reproducible methods, and to increase analytical transparency and data sharing to address the reproducibility crisis in the field.

Reward-based learning and decision-making are prime candidates to understand symptoms of attention deficit hyperactivity disorder (ADHD). However, only limited evidence is available regarding the neurocomputational underpinnings of the alterations seen in ADHD. This concerns flexible behavioral adaption in dynamically changing environments, which is challenging for individuals with ADHD. One previous study points to elevated choice switching in adolescent ADHD, which was accompanied by disrupted learning signals in medial prefrontal cortex.Here, we investigated young adults with ADHD (n = 17) as compared to age- and sex-matched controls (n = 17) using a probabilistic reversal learning experiment during functional magnetic resonance imaging (fMRI). The task requires continuous learning to guide flexible behavioral adaptation to changing reward contingencies. To disentangle the neurocomputational underpinnings of the behavioral data, we used reinforcement learning (RL) models, which informed the analysis of fMRI data.ADHD patients performed worse than controls particularly in trials before reversals, i.e., when reward contingencies were stable. This pattern resulted from ‘noisy’ choice switching regardless of previous feedback. RL modelling showed decreased reinforcement sensitivity and enhanced learning rates for negative feedback in ADHD patients. At the neural level, this was reflected in a diminished representation of choice probability in the left posterior parietal cortex in ADHD. Moreover, modelling showed a marginal reduction of learning about the unchosen option, which was paralleled by a marginal reduction in learning signals incorporating the unchosen option in the left ventral striatum.Taken together, we show that impaired flexible behavior in ADHD is due to excessive choice switching (‘hyper-flexibility’), which can be detrimental or beneficial depending on the learning environment. Computationally, this resulted from blunted sensitivity to reinforcement of which we detected neural correlates in the attention-control network, specifically in the parietal cortex. These neurocomputational findings remain preliminary due to the relatively small sample size.

Dynamic functional networks (DFN) have considerably advanced modelling of the brain communication processes. The prevailing implementation capitalizes on the system and network-level correlations between time series. However, this approach does not account for the continuous impact of non-dynamic dependencies within the statistical correlation, resulting in relatively stable connectivity patterns of DFN over time with limited sensitivity for communication dynamic between brain regions. Here, we propose an activation network framework based on the activity of functional connectivity (AFC) to extract new types of connectivity patterns during brain communication process. The AFC captures potential time-specific fluctuations associated with the brain communication processes by eliminating the non-dynamic dependency of the statistical correlation. In a simulation study, the positive correlation (r=0.966,p

Accurate, depth-resolved functional imaging is key in both understanding and treatment of the human brain. A new sonography-based imaging technique named functional Ultrasound (fUS) uniquely combines high sensitivity with submillimeter-subsecond spatiotemporal resolution available in large fields-of-view. In this proof-of-concept study we show that: (A) fUS reveals the same eloquent regions as found by fMRI while concomitantly visualizing in-vivo microvascular morphology underlying these functional hemodynamics and (B) fUS-based functional maps are confirmed by Electrocortical Stimulation Mapping (ESM), the current gold-standard in awake neurosurgical practice. This unique cross-modality experiment was performed using motor, visual and language-related functional tasks in patients undergoing awake brain tumor resection. The current work serves as an important milestone towards further maturity of fUS as well as a novel avenue to increase our understanding of hemodynamics-based functional brain imaging.

Previous literature has established the presence of sex differences in behavioral inhibition as well as its neural substrates and related disease risk. However, there is limited evidence that speaks directly to the question of whether or not there are sex-dependent associations between behavioral inhibition and resting-state brain function and, if so, how they are modulated by the underlying molecular mechanisms. We computed functional connectivity density (FCD) using resting-state functional MRI data to examine their associations with behavioral inhibition ability measured using a Go/No-Go task across a large cohort of 510 healthy young adults. Then, we examined the spatial relationships of the FCD correlates of behavioral inhibition with gene expression and neurotransmitter atlases to explore their potential genetic architecture and neurochemical basis. A significant negative correlation between behavioral inhibition and FCD in the left superior parietal lobule was found in females but not males. Further spatial correlation analyses demonstrated that the identified neural correlates of behavioral inhibition were associated with expression of gene categories predominantly implicating essential components of the cerebral cortex (glial cell, neuron, axon, dendrite, and synapse) and ion channel activity, as well as were linked to the serotonergic system. Our findings may not only yield important insights into the molecular mechanisms underlying the female-specific neural substrates of behavioral inhibition, but also provide a critical context for understanding how biological sex might contribute to variation in behavioral inhibition and its related disease risk.

Correlated fluctuations in the blood oxygenation level dependent (BOLD) signal of resting-state functional MRI (i.e., BOLD-functional connectivity, BOLD-FC) reflect a spectrum of neuronal and non-neuronal processes. In particular, there are multiple hemodynamic-vascular influences on BOLD-FC on both systemic (e.g., perfusion delay) and local levels (e.g., neurovascular coupling). While the influence of individual factors has been studied extensively, combined and comparative studies of systemic and local hemodynamic-vascular factors on BOLD-FC are scarce, notably in humans. We employed a multi-modal MRI approach to investigate and compare distinct hemodynamic-vascular processes and their impact on homotopic BOLD-FC in healthy controls and patients with unilateral asymptomatic internal carotid artery stenosis (ICAS). Asymptomatic ICAS is a cerebrovascular disorder, in which neuronal functioning is largely preserved but hemodynamic-vascular processes are impaired, mostly on the side of stenosis. Investigated indicators for local hemodynamic-vascular processes comprise capillary transit time heterogeneity (CTH) and cerebral blood volume (CBV) from dynamic susceptibility contrast (DSC) MRI, and cerebral blood flow (CBF) from pseudo-continuous arterial spin labeling (pCASL). Indicators for systemic processes are time-to-peak (TTP) from DSC MRI and BOLD lags from functional MRI. For each of these parameters, their influence on BOLD-FC was estimated by a comprehensive linear mixed model. Equally across groups, we found that individual mean BOLD-FC, local (CTH, CBV, and CBF) and systemic (TTP and BOLD lag) hemodynamic-vascular factors together explain 40.7% of BOLD-FC variance, with 20% of BOLD-FC variance explained by hemodynamic-vascular factors, with an about two-times larger contribution of systemic versus local factors. We conclude that regional differences in blood supply, i.e., systemic perfusion delays, exert a stronger influence on BOLD-FC than impairments in local neurovascular coupling.

Loud acoustic noise from the scanner during functional magnetic resonance imaging (fMRI) can affect functional connectivity (FC) observed in the resting state, but the exact effect of the MRI acoustic noise on resting state FC is not well understood. Functional ultrasound (fUS) is a neuroimaging method that visualizes brain activity based on relative cerebral blood volume (rCBV), a similar neurovascular coupling response to that measured by fMRI, but without the audible acoustic noise. In this study, we investigated the effects of different acoustic noise levels (silent, 80 dB, and 110 dB) on FC by measuring resting state fUS (rsfUS) in awake mice in an environment similar to fMRI measurement. Then, we compared the results to those of resting state fMRI (rsfMRI) conducted using an 11.7 Tesla scanner. RsfUS experiments revealed a significant reduction in FC between the retrosplenial dysgranular and auditory cortexes (0.56 ± 0.07 at silence vs 0.05 ± 0.05 at 110 dB, p=.01) and a significant increase in FC anticorrelation between the infralimbic and motor cortexes (−0.21 ± 0.08 at silence vs −0.47 ± 0.04 at 110 dB, p=.017) as acoustic noise increased from silence to 80 dB and 110 dB, with increased consistency of FC patterns between rsfUS and rsfMRI being found with the louder noise conditions. Event-related auditory stimulation experiments using fUS showed strong positive rCBV changes (16.5% ± 2.9% at 110 dB) in the auditory cortex, and negative rCBV changes (−6.7% ± 0.8% at 110 dB) in the motor cortex, both being constituents of the brain network that was altered by the presence of acoustic noise in the resting state experiments. Anticorrelation between constituent brain regions of the default mode network (such as the infralimbic cortex) and those of task-positive sensorimotor networks (such as the motor cortex) is known to be an important feature of brain network antagonism, and has been studied as a biological marker of brain disfunction and disease. This study suggests that attention should be paid to the acoustic noise level when using rsfMRI to evaluate the anticorrelation between the default mode network and task-positive sensorimotor network.

Objectives: Mapping the neurobiology of meditation has been bolstered by functional MRI (fMRI) research, with advancements in ultra-high field 7 Tesla fMRI further enhancing signal quality and neuroanatomical resolution. Here, we utilize 7 Tesla fMRI to examine the neural substrates of meditation and replicate existing widespread findings, after accounting for relevant physiological confounds. Methods: In this feasibility study, we scanned 10 beginner meditators (N = 10) while they either attended to breathing (focused attention meditation) or engaged in restful thinking (non-focused rest). We also measured and adjusted the fMRI signal for key physiological differences between meditation and rest. Finally, we explored changes in state mindfulness, state anxiety and focused attention attributes for up to 2 weeks following the single fMRI meditation session. Results: Group-level task fMRI analyses revealed significant reductions in activity during meditation relative to rest in default-mode network hubs, i.e., antero-medial prefrontal and posterior cingulate cortices, precuneus, as well as visual and thalamic regions. These findings survived stringent statistical corrections for fluctuations in physiological responses which demonstrated significant differences (p

Inhibitory control (IC) plays a critical role in cognitive and socio-emotional development. IC relies on a lateralized cortico-subcortical brain network including the inferior frontal cortex, anterior parts of insula, anterior cingulate cortex, caudate nucleus and putamen. Brain asymmetries play a critical role for IC efficiency. In parallel to age-related changes, IC can be improved following training. The aim of this study was to (1) assess the lateralization of IC network in children (N = 60, 9–10 y.o.) and (2) examine possible changes in neural asymmetry of this network from anatomical (structural MRI) and functional (resting-state fMRI) levels after 5-week computerized IC vs. active control (AC) training. We observed that IC training, but not AC training, led to a leftward lateralization of the putamen anatomy, similarly to what is observed in adults, supporting that training could accelerate the maturation of this structure.

Background: The mechanisms underlying the antidepressant effect and cognitive side effects of Electroconvulsive Therapy (ECT) remain elusive. The measurement of cerebral perfusion provides an insight into brain physiology. Objective: We investigated ECT-related perfusion changes in depressed patients and tested whether these changes correlate with clinical effects. Methods: A sample of 22 in-patients was examined at three time points: 1) within two days before, 2) within one week after, and 3) six months after an ECT series. Cerebral perfusion was quantified using arterial spin labeling magnetic resonance imaging. The primary regions of interest were the bilateral dorsolateral prefrontal cortices (DL-PFC) and hippocampi. The depression severity was assessed by the six-item Hamilton Depression Rating Scale, and cognitive performance by the Screen for Cognitive Impairment in Psychiatry. A linear mixed model and partial correlation were used for statistical analyses. Results: Following an ECT series, perfusion decreased in the right (−6.0%, p = .01) and left DL-PFC (−5.6%, p = .001). Perfusion increased in the left hippocampus (4.8%, p = .03), while on the right side the increase was insignificant (2.3%, p = .23). A larger perfusion reduction in the right DL-PFC correlated with a better antidepressant effect, and a larger perfusion increase in the right hippocampus with worse cognitive impairment. Conclusion: ECT-induced attenuation of prefrontal activity may be related to clinical improvement, whereas a hippocampal process triggered by the treatment is likely associated with cognitive side effects.

In this study we assessed the neural correlates of functional vision while varying patterns of light filtration. Four filter conditions used relatively flat filtering across the visible spectrum while one filter was a step filter that selectively absorbed violet light (wavelengths below about 415 nm). Neural effects were quantified by measuring the BOLD response ((T2*-based fMRI) while subjects performed a challenging visual task (judging gap direction in Landolt Cs that randomly varied in size). In general (based on p

Four right-handed, healthy subjects participated in a visual stimulation experiment. Subjects were viewing a dartboard-shaped flickering checkerboard stimulus, divided into 4 rings and 12 segments, defining 48 sectors in the visual field. Local contrast in each sector was continuously varying across four levels and updated every 3 s. To maintain fixation, subjects had to respond to a stimulus at the center of the visual field.During the entire experiment, in which subjects performed 8 runs, each consisting of 100 trials, brain activity was measured with functional magnetic resonance imaging (MRI). Using a 3-T Siemens Trio MRI scanner, 220 echo-planar images were acquired in each run, with a repetition time of 1.5 s and voxel size of 3 x 3 x 3 mm.The dataset is publicly available from OpenNeuro and additionally includes region of interest maps for visual areas V1 to V4, left and right, obtained from another retinotopic mapping experiment. As such, the dataset allows for accurate mapping of receptive fields and their properties across several stages of human visual cortex.

Learning in a stochastic and changing environment is a difficult task. Models of learning typically postulate that observations that deviate from the learned predictions are surprising and used to update those predictions. Bayesian accounts further posit the existence of a confidence-weighting mechanism: learning should be modulated by the confidence level that accompanies those predictions. However, the neural bases of this confidence are much less known than the ones of surprise. Here, we used a dynamic probability learning task and high-field MRI to identify putative cortical regions involved in the representation of confidence about predictions during human learning. We devised a stringent test based on the conjunction of four criteria. We localized several regions in parietal and frontal cortices whose activity is sensitive to the confidence of an ideal observer, specifically so with respect to potential confounds (surprise and predictability), and in a way that is invariant to which item is predicted. We also tested for functionality in two ways. First, we localized regions whose activity patterns at the subject level showed an effect of both confidence and surprise in qualitative agreement with the confidence-weighting principle. Second, we found neural representations of ideal confidence that also accounted for subjective confidence. Taken together, those results identify a set of cortical regions potentially implicated in the confidence-weighting of learning.

The midbrain periaqueductal grey (PAG) is a critical region for the mediation of pain-related behavioural responses. Neuronal tract tracing techniques in experimental animal studies have demonstrated that the lateral column of the PAG (lPAG) displays a crude somatotopy, which is thought to be critical for the selection of contextually appropriate behavioural responses, without the need for higher brain input. In addition to the different behavioural responses to cutaneous and muscle pain – active withdrawal versus passive coping – there is evidence that cutaneous pain is processed in the region of the lPAG and muscle pain in the adjacent ventrolateral PAG (vlPAG). Given the fundamental nature of these behavioural responses to cutaneous and muscle pain, these PAG circuits are assumed to have been preserved, though yet to be definitively documented in humans. Using ultra-high field (7-Tesla) functional magnetic resonance imaging we determined the locations of signal intensity changes in the PAG during noxious cutaneous heat stimuli and muscle pain in healthy control participants. Images were processed and blood oxygen level dependant (BOLD) signal changes within the PAG determined. It was observed that noxious cutaneous stimulation of the lip, cheek, and ear evoked maximal increases in BOLD activation in the rostral contralateral PAG, whereas noxious cutaneous stimulation of the thumb and toe evoked increases in the caudal contralateral PAG. Analysis of individual participants demonstrated that these activations were located in the lPAG. Furthermore, we found that deep muscular pain evoked the greatest increases in signal intensity in the vlPAG. These data suggest that the crude somatotopic organization of the PAG may be phyletically preserved between experimental animals and humans, with a body-face delineation capable of producing an appropriate behavioural response based on the location and tissue origin of a noxious stimulus.

Background: Reward processing deficits are a core feature of schizophrenia and are thought to underlie negative symptoms. Pre-clinical evidence suggests that opioid neurotransmission is linked to reward processing. However, the contribution of Mu Opioid Receptor (MOR) signalling to the reward processing abnormalities in schizophrenia is unknown. Here, we examined the association between MOR availability and the neural processes underlying reward anticipation in patients with schizophrenia using multimodal neuroimaging. Method: 37 subjects (18 with Schizophrenia with moderate severity negative symptoms and 19 age and sex-matched healthy controls) underwent a functional MRI scan while performing the Monetary Incentive Delay (MID) task to measure the neural response to reward anticipation. Participants also had a [11C]-carfentanil PET scan to measure MOR availability. Results: Reward anticipation was associated with increased neural activation in a widespread network of brain regions including the striatum. Patients with schizophrenia had both significantly lower MOR availability in the striatum as well as striatal hypoactivation during reward anticipation. However, there was no association between MOR availability and striatal neural activity during reward anticipation in either patient or controls (Pearson's Correlation, controls df = 17, r = 0.321, p = 0.18, patients df = 16, r = 0.295, p = 0.24). There was no association between anticipation-related neural activation and negative symptoms (r = –0.120, p = 0.14) or anhedonia severity (social r = –0.365, p = 0.14 physical r = –0.120, p = 0.63). Conclusions: Our data suggest reduced MOR availability in schizophrenia might not underlie striatal hypoactivation during reward anticipation in patients with established illness. Therefore, other mechanisms, such as dopamine dysfunction, warrant further investigation as treatment targets for this aspect of the disorder.

Functional MRI is an essential component of presurgical language mapping. In clinical settings, young children may be sedated for the MRI with the functional stimuli presented passively. Research has found that sedation changes language activation in healthy adults and children. However, there is limited research comparing sedated and unsedated functional MRI in pediatric epilepsy patients. We compared language activation patterns in children with epilepsy who received sedation for functional MRI to the ones who did not.We retrospectively identified the patients with focal epilepsy who underwent presurgical functional MRI including Auditory Descriptive Decision Task at Boston Children's Hospital from 2014 to 2022. Patients were divided into sedated and awake groups, based on their sedation status during functional MRI. Auditory Descriptive Decision Task stimuli were presented passively to the sedated group per clinical protocol. We extracted language activation maps contrasted against a control task (reverse speech) in the Frontal and Temporal language regions and calculated separate language laterality indexes for each region. We considered positive laterality indexes as left dominant, negative laterality indexes as right dominant, and absolute laterality indexes

An emerging body of work has revealed alterations in structural (SC) and functional (FC) brain connectivity following mild TBI (mTBI), with mixed findings. However, these studies seldom integrate complimentary neuroimaging modalities within a unified framework. Multilayer network analysis is an emerging technique to uncover how white matter organization enables functional communication. Using our novel graph metric (SC-FC Bandwidth), we quantified the information capacity of synchronous brain regions in 53 mild TBI patients (46 females; age mean = 40.2 years (y), σ = 16.7 (y), range: 18–79 (y). Diffusion MRI and resting state fMRI were administered at the acute and chronic post-injury intervals. Moreover, participants completed a cognitive task to measure processing speed (30 Seconds and Counting Task; 30-SACT). Processing speed was significantly increased at the chronic, relative to the acute post-injury intervals (p =

Background: Functional connectivity has been associated with psychiatric problems, both in children and adults, but inconsistencies are present across studies. Prior research has mostly focused on small clinical samples with cross-sectional designs. Methods: We adopted a longitudinal design with repeated assessments to investigate associations between functional network connectivity (FNC) and psychiatric problems in youth (9- to 17-year-olds, two time points) from the general population. The largest single-site study of pediatric neurodevelopment was used: Generation R (N = 3,131 with data at either time point). Psychiatric symptoms were measured with the Child Behavioral Checklist as broadband internalizing and externalizing problems, and its eight specific syndrome scales (e.g., anxious-depressed). FNC was assessed with two complementary approaches. First, static FNC (sFNC) was measured with graph theory-based metrics. Second, dynamic FNC (dFNC), where connectivity is allowed to vary over time, was summarized into 5 states that participants spent time in. Cross-lagged panel models were used to investigate the longitudinal bidirectional relationships of sFNC with internalizing and externalizing problems. Similar cross-lagged panel models were run for dFNC. Results: Small longitudinal relationships between dFNC and certain syndrome scales were observed, especially for baseline syndrome scales (i.e., rule-breaking, somatic complaints, thought problems, and attention problems) predicting connectivity changes. However, no association between any of the psychiatric problems (broadband and syndrome scales) with either measure of FNC survived correction for multiple testing. Conclusion: We found no or very modest evidence for longitudinal associations between psychiatric problems with dynamic and static FNC in this population-based sample. Differences in findings may stem from the population drawn, study design, developmental timing, and sample sizes.

Background: Impulse control behaviors (ICB) are frequently observed in patients with Parkinson’s disease (PD) and are characterized by compulsive and repetitive behavior resulting from the inability to resist internal drives. Objectives: In this study, we aimed to provide a better understanding of structural and functional brain alterations and clinical parameters related to ICB in PD patients. Methods: We utilized a dataset from the Parkinson’s Progression Markers Initiative including 36 patients with ICB (PDICB+) compared to 76 without ICB (PDICB-) and 61 healthy controls (HC). Using multimodal MRI data we assessed gray matter brain volume, white matter integrity, and graph topological properties at rest. Results: Compared with HC, PDICB+ showed reduced gray matter volume in the bilateral superior and middle temporal gyrus and in the right middle occipital gyrus. Compared with PDICB-, PDICB+ showed volume reduction in the left anterior insula. Depression and anxiety were more prevalent in PDICB+ than in PDICB- and HC. In PDICB+, lower gray matter volume in the precentral gyrus and medial frontal cortex, and higher axial diffusivity in the superior corona radiata were related to higher depression score. Both PD groups showed disrupted functional topological network pattern within the cingulate cortex compared with HC. PDICB+ vs PDICB- displayed reduced topological network pattern in the anterior cingulate cortex, insula, and nucleus accumbens. Conclusions: Our results suggest that structural alterations in the insula and abnormal topological connectivity pattern in the salience network and the nucleus accumbens may lead to impaired decision making and hypersensitivity towards reward in PDICB+. Moreover, PDICB+ are more prone to suffer from depression and anxiety.

Individuals differ in their functional connectome, which can be demonstrated using a “fingerprinting” analysis in which the connectome from an individual in one dataset is used to identify the same person from an independent dataset. Recently, the origin of these fingerprints has been studied by examining if they are present in infants. The results have varied considerably, with identification rates from 10 to 90%. When fingerprinting has been performed by splitting a single imaging session into two split-sessions (within session), identification rates were higher than when two full-sessions (between sessions) were compared. This study examined whether a methodological difference could account for this variation. It was hypothesized that the infant's exact head position in the head coil may affect the measured connectome, due to the gradual inhomogeneity of signal-to-noise in phased-array coils and the breadth of possible positions for a small infant head in a head coil. This study examined the impact of this using resting state functional MRI data from the Developing Human Connectome Project second release. Using functional timeseries, fingerprinting identification was high (84-91%) within a session while between sessions it was low (7%).Using N = 416 infants’ head positions, a map of the average signal-to-noise across the physical volume of the head coil was calculated and was used (independent group of 44 infants with two scan sessions) to demonstrate a significant relationship between head position in the head coil and functional connectivity. Using only the head positions (signal-to-noise values extrapolated from the group average map) of the independent group of 44 infants, high identification success was achieved across split-sessions (within session) but not full-sessions (between sessions). Using a model examining factors influencing the stability of the functional connectome, head position was seen as the strongest of the explanatory variables. We conclude within-session fingerprinting is affected by head position and future infant functional fingerprint analyses must use a different strategy or account for this impact.

The lateral geniculate nucleus (LGN) is a key thalamic nucleus in the visual system, which has an important function in relaying retinal visual input to the visual cortex. The human LGN is composed mainly of magnocellular (M) and parvocellular (P) subdivisions, each of which has different stimulus selectivity in neural response properties. Previous studies have discussed the potential relationship between LGN subdivisions and visual disorders based on psychophysical data on specific types of visual stimuli. However, these relationships remain speculative because non-invasive measurements of these subdivisions are difficult due to the small size of the LGN. Here we propose a method to identify these subdivisions by combining two structural MR measures: high-resolution proton-density weighted images and macromolecular tissue volume (MTV) maps. We defined the M and P subdivisions based on MTV fraction data and tested the validity of the definition by (1) comparing the data with that from human histological studies, (2) comparing the data with functional magnetic resonance imaging measurements on stimulus selectivity, and (3) analyzing the test-retest reliability. The findings demonstrated that the spatial organization of the M and P subdivisions was consistent across subjects and in line with LGN subdivisions observed in human histological data. Moreover, the difference in stimulus selectivity between the subdivisions identified using MTV was consistent with previous physiology literature. The definition of the subdivisions based on MTV was shown to be robust over measurements taken on different days. These results suggest that MTV mapping is a promising approach for evaluating the tissue properties of LGN subdivisions in living humans. This method potentially will enable neuroscientific and clinical hypotheses about the human LGN subdivisions to be tested.

The human brain undergoes rapid changes from the fetal stage to two years postnatally, during which proper structural and functional maturation lays the foundation for later cognitive and behavioral development. Multimodal magnetic resonance imaging (MRI) techniques, especially structural MRI (sMRI), diffusion MRI (dMRI), functional MRI (fMRI), and perfusion MRI (pMRI), provide unprecedented opportunities to non-invasively quantify these early brain changes at whole brain and regional levels. Each modality offers unique insights into the complex processes of both typical neurodevelopment and the pathological mechanisms underlying psychiatric and neurological disorders. Compared to a single modality, multimodal MRI enhances discriminative power and provides more comprehensive insights for understanding and improving neurodevelopmental and mental health outcomes, particularly in high-risk populations. Machine learning- and deep learning-based methods have demonstrated significant potential for predicting future outcomes using multimodal brain MRI acquired during early childhood. Here, we review the unique characteristics of various MRI techniques for imaging early brain development and describe the common approaches to analyze these modalities. We then discuss machine learning approaches in predicting future neurodevelopmental and clinical outcomes using multimodal MRI information during early childhood, highlighting the potential of identifying biomarkers for early detection and personalized interventions in atypical development., Competing Interests: Declaration of competing interest The authors declare no competing interests., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Introduction: Intravenous lidocaine is increasingly used as a nonopioid analgesic, but how it acts in the brain is incompletely understood. We conducted a functional MRI study of pain response, resting connectivity, and cognitive task performance in volunteers to elucidate the effects of lidocaine at the brain-systems level., Methods: We enrolled 27 adults (age 22-55 yr) in this single-arm, open-label study. Pain response task and resting-state functional MRI scans at 3 T were obtained at baseline and then with a constant effect-site concentration of lidocaine. Electric nerve stimulation, titrated in advance to 7/10 intensity, was used for the pain task (five times every 10 s). Group-level differences in pain task-evoked responses (primary outcome, focused on the insula) and in resting connectivity were compared between baseline and lidocaine conditions, using adjusted P<0.05 to account for multiple comparisons. Pain ratings and performance on a brief battery of computer-based tasks were also recorded., Results: Lidocaine infusion was associated with decreased pain-evoked responses in the insula (left: Z=3.6, P<0.001, right: Z=3.6, P=0.004) and other brain areas including the cingulate gyrus, thalamus, and primary sensory cortex. Resting-state connectivity showed significant diffuse reductions in both region-to-region and global connectivity measures with lidocaine. Small decreases in pain intensity and unpleasantness and worse memory performance were also seen with lidocaine., Conclusions: Lidocaine was associated with broad reductions in functional MRI response to acute pain and modulated whole-brain functional connectivity, predominantly decreasing long-range connectivity. This was accompanied by small but significant decreases in pain perception and memory performance., Clinical Trial Registration: NCT05501600., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Humans have an extraordinary ability to recognize facial expression and identity from a single face simultaneously and effortlessly, however, the underlying neural computation is not well understood. Here, we optimized a multi-task deep neural network to classify facial expression and identity simultaneously. Under various optimization training strategies, the best-performing model consistently showed ‘share-separate’ organization. The two separate branches of the best-performing model also exhibited distinct abilities to categorize facial expression and identity, and these abilities increased along the facial expression or identity branches toward high layers. By comparing the representational similarities between the best-performing model and functional magnetic resonance imaging (fMRI) responses in the human visual cortex to the same face stimuli, the face-selective posterior superior temporal sulcus (pSTS) in the dorsal visual cortex was significantly correlated with layers in the expression branch of the model, and the anterior inferotemporal cortex (aIT) and anterior fusiform face area (aFFA) in the ventral visual cortex were significantly correlated with layers in the identity branch of the model. Besides, the aFFA and aIT better matched the high layers of the model, while the posterior FFA (pFFA) and occipital facial area (OFA) better matched the middle and early layers of the model, respectively. Overall, our study provides a task-optimization computational model to better understand the neural mechanism underlying face recognition, which suggest that similar to the best-performing model, the human visual system exhibits both dissociated and hierarchical neuroanatomical organization when simultaneously coding facial identity and expression.

A fundamental goal across the neurosciences is the characterization of relationships linking brain anatomy, functioning, and behavior. Although various MRI modalities have been developed to probe these relationships, direct comparisons of their ability to predict behavior have been lacking. Here, we compared the ability of anatomical T1, diffusion and functional MRI (fMRI) to predict behavior at an individual level. Cortical thickness, area and volume were extracted from anatomical T1 images. Diffusion Tensor Imaging (DTI) and approximate Neurite Orientation Dispersion and Density Imaging (NODDI) models were fitted to the diffusion images. The resulting metrics were projected to the Tract-Based Spatial Statistics (TBSS) skeleton. We also ran probabilistic tractography for the diffusion images, from which we extracted the stream count, average stream length, and the average of each DTI and NODDI metric across tracts connecting each pair of brain regions. Functional connectivity (FC) was extracted from both task and resting-state fMRI. Individualized prediction of a wide range of behavioral measures were performed using kernel ridge regression, linear ridge regression and elastic net regression. Consistency of the results were investigated with the Human Connectome Project (HCP) and Adolescent Brain Cognitive Development (ABCD) datasets. In both datasets, FC-based models gave the best prediction performance, regardless of regression model or behavioral measure. This was especially true for the cognitive component. Furthermore, all modalities were able to predict cognition better than other behavioral components. Combining all modalities improved prediction of cognition, but not other behavioral components. Finally, across all behaviors, combining resting and task FC yielded prediction performance similar to combining all modalities. Overall, our study suggests that in the case of healthy children and young adults, behaviorally-relevant information in T1 and diffusion features might reflect a subset of the variance captured by FC.

ABSTRACT: A deep understanding of the neural architecture of mental function should enable the accurate prediction of a specific pattern of brain activity for any psychological task, based only on the cognitive functions known to be engaged by that task. Encoding models (EMs), which predict neural responses from known features (e.g., stimulus properties), have succeeded in circumscribed domains (e.g., visual neuroscience), but implementing domain-general EMs that predict brain-wide activity for arbitrary tasks has been limited mainly by availability of datasets that 1) sufficiently span a large space of psychological functions, and 2) are sufficiently annotated with such functions to allow robust EM specification. We examine the use of EMs based on a formal specification of psychological function, to predict cortical activation patterns across a broad range of tasks. We utilized the Multi-Domain Task Battery, a dataset in which 24 subjects completed 32 ten-minute fMRI scans, switching tasks every 35 s and engaging in 44 total conditions of diverse psychological manipulations. Conditions were annotated by a group of experts using the Cognitive Atlas ontology to identify putatively engaged functions, and region-wise cognitive EMs (CEMs) were fit, for individual subjects, on neocortical responses. We found that CEMs predicted cortical activation maps of held-out tasks with high accuracy, outperforming a permutation-based null model while approaching the noise ceiling of the data, without being driven solely by either cognitive or perceptual-motor features. Hierarchical clustering on the similarity structure of CEM generalization errors revealed relationships amongst psychological functions. Spatial distributions of feature importances systematically overlapped with large-scale resting-state functional networks (RSNs), supporting the hypothesis of functional specialization within RSNs while grounding their function in an interpretable data-driven manner. Our implementation and validation of CEMs provides a proof of principle for the utility of formal ontologies in cognitive neuroscience and motivates the use of CEMs in the further testing of cognitive theories.

Background: Classic psychedelics, such as psilocybin and LSD, and other serotonin 2A receptor (5-HT2AR) agonists evoke acute alterations in perception and cognition. Altered thalamocortical connectivity has been hypothesized to underlie these effects, which is supported by some functional MRI (fMRI) studies. These studies have treated the thalamus as a unitary structure, despite known differential 5-HT2AR expression and functional specificity of different intrathalamic nuclei. Independent Component Analysis (ICA) has been previously used to identify reliable group-level functional subdivisions of the thalamus from resting-state fMRI (rsfMRI) data. We build on these efforts with a novel data-maximizing ICA-based approach to examine psilocybin-induced changes in intrathalamic functional organization and thalamocortical connectivity in individual participants. Methods: Baseline rsfMRI data (n=38) from healthy individuals with a long-term meditation practice was utilized to generate a statistical template of thalamic functional subdivisions. This template was then applied in a novel ICA-based analysis of the acute effects of psilocybin on intra- and extra-thalamic functional organization and connectivity in follow-up scans from a subset of the same individuals (n=18). We examined correlations with subjective reports of drug effect and compared with a previously reported analytic approach (treating the thalamus as a single functional unit). Results: Several intrathalamic components showed significant psilocybin-induced alterations in spatial organization, with effects of psilocybin largely localized to the mediodorsal and pulvinar nuclei. The magnitude of changes in individual participants correlated with reported subjective effects. These components demonstrated predominant decreases in thalamocortical connectivity, largely with visual and default mode networks. Analysis in which the thalamus is treated as a singular unitary structure showed an overall numerical increase in thalamocortical connectivity, consistent with previous literature using this approach, but this increase did not reach statistical significance. Conclusions: We utilized a novel analytic approach to discover psilocybin-induced changes in intra- and extra-thalamic functional organization and connectivity of intrathalamic nuclei and cortical networks known to express the 5-HT2AR. These changes were not observed using whole-thalamus analyses, suggesting that psilocybin may cause widespread but modest increases in thalamocortical connectivity that are offset by strong focal decreases in functionally relevant intrathalamic nuclei.

Over the past two decades, magnetic resonance imaging (MRI) studies have explored brain activation patterns during acute noxious stimuli. Whilst these human investigations have detailed changes in primarily cortical regions, they have generally not explored discrete changes within small brain areas that are critical in driving behavioural, autonomic, and endocrine responses to pain, such as within subregions of the hypothalamus, amygdala, and midbrain periaqueductal gray matter (PAG). Ultra-high field (7-Tesla) MRI provides enough signal-to-noise at high spatial resolutions to investigate activation patterns within these small brain regions during acute noxious stimulation in awake humans. In this study we used 7T functional MRI to concentrate on hypothalamic, amygdala, and PAG signal changes during acute noxious orofacial stimuli. Noxious heat stimuli were applied in three separate fMRI scans to three adjacent sites on the face in 16 healthy control participants (7 females). Images were processed using SPM12 and custom software, and blood oxygen level dependent signal changes within the hypothalamus, amygdala, and PAG assessed. We identified altered activity within eight unique subregions of the hypothalamus, four unique subregions of the amygdala, and a single region in the lateral PAG. Specifically, within the hypothalamus and amygdala, signal intensity largely decreased during noxious stimulation, and increased in the lateral PAG. Furthermore, we found sex-related differences in discrete regions of the hypothalamus and amygdala. This study reveals that the activity of discrete nuclei during acute noxious thermal stimulation in awake humans.

Wakefulness levels modulate estimates of functional connectivity (FC), and, if unaccounted for, can become a substantial confound in resting-state fMRI. Unfortunately, wakefulness is rarely monitored due to the need for additional concurrent recordings (e.g., eye tracking, EEG). Recent work has shown that strong fluctuations around 0.05Hz, hypothesized to be CSF inflow, appear in the fourth ventricle (FV) when subjects fall asleep, and that they correlate significantly with the global signal. The analysis of these fluctuations could provide an easy way to evaluate wakefulness in fMRI-only data and improve our understanding of FC during sleep. Here we evaluate this possibility using the 7T resting-state sample from the Human Connectome Project (HCP). Our results replicate the observation that fourth ventricle ultra-slow fluctuations (∼0.05Hz) with inflow-like characteristics (decreasing in intensity for successive slices) are present in scans during which subjects did not comply with instructions to keep their eyes open (i.e., drowsy scans). This is true despite the HCP data not being optimized for the detection of inflow-like effects. In addition, time-locked BOLD fluctuations of the same frequency could be detected in large portions of grey matter with a wide range of temporal delays and contribute in significant ways to our understanding of how FC changes during sleep. First, these ultra-slow fluctuations explain half of the increase in global signal that occurs during descent into sleep. Similarly, global shifts in FC between awake and sleep states are driven by changes in this slow frequency band. Second, they can influence estimates of inter-regional FC. For example, disconnection between frontal and posterior components of the Defulat Mode Network (DMN) typically reported during sleep were only detectable after regression of these ultra-slow fluctuations. Finally, we report that the temporal evolution of the power spectrum of these ultra-slow FV fluctuations can help us reproduce sample-level sleep patterns (e.g., a substantial number of subjects descending into sleep 3 minutes following scanning onset), partially rank scans according to overall drowsiness levels, and predict individual segments of elevated drowsiness (at 60 seconds resolution) with 71% accuracy.

Top-down processes such as expectations play a key role in pain perception. In specific contexts, inferred threat of impending pain can affect perceived pain more than the noxious intensity. This biasing effect of top-down threats can affect some individuals more strongly than others due to differences in fear of pain. The specific characteristics of intrinsic brain characteristics that mediate the effects of top-down threat bias are mainly unknown. In this study, we examined whether threat bias is associated with structural and functional brain connectivity. The variability in the top-down bias was mapped to the microstructure of white matter in diffusion weighted images (DWI) using MRTrix3. Mean functional connectivity of five canonical resting state networks was tested for association with bias scores and with the identified DWI metrics. We found that the fiber density of the splenium of the corpus callosum was significantly low in individuals with high top-down threat bias (FWE corrected with 5000 permutations, p

A general linear model is widely used for analyzing fMRI data, in which the blood oxygenation-level dependent (BOLD) signals in gray matter (GM) evoked in response to neural stimulation are modeled by convolving the time course of the expected neural activity with a canonical hemodynamic response function (HRF) obtained a priori. The maps of brain activity produced reflect the magnitude of local BOLD responses. However, detecting BOLD signals in white matter (WM) is more challenging as the BOLD signals are weaker and the HRF is different, and may vary more across the brain. Here we propose a model-free approach to detect changes in BOLD signals in WM by measuring task-evoked increases of BOLD signal synchrony in WM fibers. The proposed approach relies on a simple assumption that, in response to a functional task, BOLD signals in relevant fibers are modulated by stimulus-evoked neural activity and thereby show greater synchrony than when measured in a resting state, even if their magnitudes do not change substantially. This approach is implemented in two technical stages. First, for each voxel a fiber-architecture-informed spatial window is created with orientation distribution functions constructed from diffusion imaging data. This provides the basis for defining neighborhoods in WM that share similar local fiber architectures. Second, a modified principal component analysis (PCA) is used to estimate the synchrony of BOLD signals in each spatial window. The proposed approach is validated using a 3T fMRI dataset from the Human Connectome Project (HCP) at a group level. The results demonstrate that neural activity can be reliably detected as increases in fMRI signal synchrony within WM fibers that are engaged in a task with high sensitivities and reproducibility.

Representational Similarity Analysis (RSA) has emerged as a popular method for relating representational spaces from human brain activity, behavioral data, and computational models. RSA is based on the comparison of representational (dis-)similarity matrices (RDMs or RSMs), which characterize the pairwise (dis-)similarities of all conditions across all features (e.g. fMRI voxels or units of a model). However, classical RSA treats each feature as equally important. This ‘equal weights’ assumption contrasts with the flexibility of multivariate decoding, which reweights individual features for predicting a target variable. As a consequence, classical RSA may lead researchers to underestimate the correspondence between a model and a brain region and, in case of model comparison, may lead them to select an inferior model. The aim of this work is twofold: First, we sought to broadly test feature-reweighted RSA (FR-RSA) applied to computational models and reveal the extent to which reweighting model features improves RSM correspondence and affects model selection. Previous work suggested that reweighting can improve model selection in RSA but it has remained unclear to what extent these results generalize across datasets and data modalities. To draw more general conclusions, we utilized a range of publicly available datasets and three popular deep neural networks (DNNs). Second, we propose voxel-reweighted RSA, a novel use case of FR-RSA that reweights fMRI voxels, mirroring the rationale of multivariate decoding of optimally combining voxel activity patterns. We found that reweighting individual model units markedly improved the fit between model RSMs and target RSMs derived from several fMRI and behavioral datasets and affected model selection, highlighting the importance of considering FR-RSA. For voxel-reweighted RSA, improvements in RSM correspondence were even more pronounced, demonstrating the utility of this novel approach. We additionally show that classical noise ceilings can be exceeded when FR-RSA is applied and propose an updated approach for their computation. Taken together, our results broadly validate the use of FR-RSA for improving the fit between computational models, brain, and behavioral data, allowing us to better adjudicate between competing computational models. Further, our results suggest that FR-RSA applied to brain measurement channels could become an important new method to assess the correspondence between representational spaces.

Imaging the infant brain with MRI has improved our understanding of early neurodevelopment. However, head motion during MRI acquisition is detrimental to both functional and structural MRI scan quality. Though infants are typically scanned while asleep, they commonly exhibit motion during scanning causing data loss. Our group has shown that providing MRI technicians with real-time motion estimates via Framewise Integrated Real-Time MRI Monitoring (FIRMM) software helps obtain high-quality, low motion fMRI data. By estimating head motion in real time and displaying motion metrics to the MR technician during an fMRI scan, FIRMM can improve scanning efficiency. Here, we compared average framewise displacement (FD), a proxy for head motion, and the amount of usable fMRI data (FD ≤ 0.2 mm) in infants scanned with (n = 407) and without FIRMM (n = 295). Using a mixed-effects model, we found that the addition of FIRMM to current state-of-the-art infant scanning protocols significantly increased the amount of usable fMRI data acquired per infant, demonstrating its value for research and clinical infant neuroimaging.

In the mature brain, structural and functional ‘fingerprints’ of brain connectivity can be used to identify the uniqueness of an individual. However, whether the characteristics that make a given brain distinguishable from others already exist at birth remains unknown. Here, we used neuroimaging data from the developing Human Connectome Project (dHCP) of preterm born neonates who were scanned twice during the perinatal period to assess the developing brain fingerprint. We found that 62% of the participants could be identified based on the congruence of the later structural connectome to the initial connectivity matrix derived from the earlier timepoint. In contrast, similarity between functional connectomes of the same subject at different time points was low. Only 10% of the participants showed greater self-similarity in comparison to self-to-other-similarity for the functional connectome. These results suggest that structural connectivity is more stable in early life and can represent a potential connectome fingerprint of the individual: a relatively stable structural connectome appears to support a changing functional connectome at a time when neonates must rapidly acquire new skills to adapt to their new environment.

Background: The amount of people affected by traffic-related air pollution and noise is continuously increasing, but limited research has been conducted on the association between these environmental exposures and functional brain connectivity in children. Objective: This exploratory study aimed to analyze the associations between the exposure to traffic-related air pollution and noise during pregnancy and childhood, and functional brain connectivity amongst a wide-swath of brain areas in preadolescents from 9 to 12 years of age. Methods: We used data of 2,197 children from the Generation R Study. Land use regression models were applied to estimate nitrogen oxides and particulate matter levels at participant’s homes for several time periods: pregnancy, birth to 3 years, 3 to 6 years, and 6 years of age to the age at magnetic resonance imaging (MRI) assessment. Existing noise maps were used to estimate road traffic noise exposure at participant’s homes for the same time periods. Resting-state functional MRI was obtained at 9–12 years of age. Pair-wise correlation coefficients of the blood-oxygen-level-dependent signals between 380 brain areas were calculated. Linear regressions were run and corrected for multiple testing. Results: Preadolescents exposed to higher levels of NO2, NOx, and PM2.5 absorbance, from birth to 3 years, and from 3 to 6 years of age showed higher correlation coefficients among several brain regions (e.g. from 0.16 to 0.19 higher correlation coefficient related to PM2.5 absorbance exposure, depending on the brain connection). Overall, most identified associations were between brain regions of the task positive and task negative networks, and were mainly inter-network (20 of 26). Slightly more than half of the connections were intra-hemispheric (14 of 26), predominantly in the right hemisphere. Road traffic noise was not associated with functional brain connectivity. Conclusions: This exploratory study found that exposure to traffic-related air pollution during the first years of life was related to higher functional brain connectivity predominantly in brain areas located in the task positive and task negative networks, in preadolescents from 9 to 12 years of age. These results could be an indicator of differential functional connectivity in children exposed to higher levels of air pollution.

Simultaneous multi-slice (multiband) accelerated functional magnetic resonance imaging (fMRI) provides dramatically improved temporal and spatial resolution for resting-state functional connectivity (RSFC) studies of the human brain in health and disease. However, multiband acceleration also poses unique challenges for denoising of subject motion induced data artifacts, the presence of which is a major confound in RSFC research that substantively diminishes reliability and reproducibility. We comprehensively evaluated existing and novel approaches to volume censoring-based motion denoising in the Human Connectome Project (HCP) dataset. We show that assumptions underlying common metrics for evaluating motion denoising pipelines, especially those based on quality control-functional connectivity (QC-FC) correlations and differences between high- and low-motion participants, are problematic, and appear to be inappropriate in their current widespread use as indicators of comparative pipeline performance and as targets for investigators to use when tuning pipelines for their own datasets. We further develop two new quantitative metrics that are instead agnostic to QC-FC correlations and other measures that rely upon the null assumption that no true relationships exist between trait measures of subject motion and functional connectivity, and demonstrate their use as benchmarks for comparing volume censoring methods. Finally, we develop and validate quantitative methods for determining dataset-specific optimal volume censoring parameters prior to the final analysis of a dataset, and provide straightforward recommendations and code for all investigators to apply this optimized approach to their own RSFC datasets.

Believing as a fundamental mental process influences other cognitive/affective processes and behavior. However, it is unclear whether believing engages distinct neurocognitive mechanisms in people with different cultural experiences. We addressed this issue by scanning Chinese and Danish adults using functional MRI during believing judgments on personality traits of oneself and a celebrity. Drift diffusion model analyses of behavioral performances revealed that speed/quality of information acquisition varied between believing judgments on positive and negative personality traits in Chinese but not in Danes. Chinese adopted a more conservative strategy of decision-making during celebrity- than self-believing judgments whereas an opposite pattern was observed in Danes. Non-decisional processes were longer for celebrity- than for self-believing in Danes but not in Chinese. Believing judgments activated the medial prefrontal cortex (mPFC) in both cultural groups but elicited stronger left anterior insular and ventral frontal activations in Chinese. Greater mPFC activity in Chinese was associated with longer duration of non-decision processes during believing-judgments, which predicted slower retrieval of self-related information in a memory test. Greater mPFC activity in Danes, however, was associated with a less degree of adopting a conservative strategy during believing judgments, which predicted faster retrieval of self-related information. Our findings highlight different neurocognitive processes engaged in believing between individuals from East Asian and Western cultures.

Background and objective: Repetitive transcranial magnetic stimulation (rTMS) is a first-line treatment for treatment-resistant depression (TRD). The mechanisms of action of rTMS are not fully understood, and no biomarkers are available to assist in clinical practice to predict response to rTMS. This study aimed to demonstrate that after-rTMS clinical improvement is associated with functional connectivity (FC) changes of the subgenual cingulate cortex (sgACC) and rostral anterior cingulate (rACC), and FC of sgACC and rACC might serve as potential predictors for treatment response. Methods: Resting-state functional magnetic resonance imaging (rs-fMRI) data were collected within 1 week before rTMS initiation in 50 TRD patients to predict subsequent response to rTMS on the left dorsolateral prefrontal cortex (DLPFC). Follow-up rs-fMRI was obtained 12 weeks after completion of rTMS and neural correlates of rTMS in sgACC- and rACC-related FC patterns were compared to before rTMS data and with rs-fMRI from healthy participants. Results: Treatment response was associated with lower FC of sgACC to right DLPFC and higher FC of rACC to left lateral parietal cortex (IPL) measured at baseline. Using sgACC-DLPFC and rACC-IPL connectivity as features, responder-nonresponder classification accuracies of 84% and 76% (end-of-treatment), 88% and 81% (3-month follow-up), respectively were achieved. Longitudinal rs-fMRI data analyses revealed that the hyperconnectivity between sgACC and visual cortex was normalized to a level which was comparable to that of healthy participants. Conclusions: Brain activity patterns in depression are predictive of treatment response to rTMS, and longitudinal change of brain activity in relevant brain circuits after rTMS is associated with treatment response in depression. Target engagement paradigms may offer opportunities to increase the efficacy of rTMS in TRD by optimal selection of patients for treatment. Trial registration: ClinicalTrials.gov Identifiers: NCT01887782 and NCT02800226.

After falling asleep, the brain needs to detach from waking activity and reorganize into a functionally distinct state. A functional MRI (fMRI) study has recently revealed that the transition to unconsciousness induced by propofol involves a global decline of brain activity followed by a transient reduction in cortico-subcortical coupling. We have analyzed the relationships between transitional brain activity and breathing changes as one example of a vital function that needs the brain to readapt. Thirty healthy participants were originally examined. The analysis involved the correlation between breathing and fMRI signal upon loss of consciousness. We proposed that a decrease in ventilation would be coupled to the initial decline in fMRI signal in brain areas relevant for modulating breathing in the awake state, and that the subsequent recovery would be coupled to fMRI signal in structures relevant for controlling breathing during the unconscious state. Results showed that a slight reduction in breathing from wakefulness to unconsciousness was distinctively associated with decreased activity in brain systems underlying different aspects of consciousness including the prefrontal cortex, the default mode network and somatosensory areas. Breathing recovery was distinctively coupled to activity in deep brain structures controlling basic behaviors such as the hypothalamus and amygdala. Activity in the brainstem, cerebellum and hippocampus was associated with breathing variations in both states. Therefore, our brain maps illustrate potential drives to breathe, unique to wakefulness, in the form of brain systems underlying cognitive awareness, self-awareness and sensory awareness, and to unconsciousness involving structures controlling instinctive and homeostatic behaviors.

Spinal cord magnetic resonance imaging (MRI) has a central role in multiple sclerosis (MS) clinical practice for diagnosis and disease monitoring. Advanced MRI sequences capable of visualizing and quantifying tissue macro- and microstructure and reflecting different pathological disease processes have been used in MS research; however, the spinal cord remains under-explored, partly due to technical obstacles inherent to imaging this structure. We propose that the study of the spinal cord merits equal ambition in overcoming technical challenges, and that there is much information to be exploited to make valuable contributions to our understanding of MS.We present a narrative review on the latest progress in advanced spinal cord MRI in MS, covering in the first part structural, functional, metabolic and vascular imaging methods. We focus on recent studies of MS and those making significant technical steps, noting the challenges that remain to be addressed and what stands to be gained from such advances. Throughout we also refer to other works that presend more in-depth review on specific themes. In the second part, we present several topics that, in our view, hold particular potential. The need for better imaging of gray matter is discussed. We stress the importance of developing imaging beyond the cervical spinal cord, and explore the use of ultra-high field MRI. Finally, some recommendations are given for future research, from study design to newer developments in analysis, and the need for harmonization of sequences and methods within the field.This review is aimed at researchers and clinicians with an interest in gaining an overview of the current state of advanced MRI research in this field and what is primed to be the future of spinal cord imaging in MS research.

Background: Negative bias—a mood-congruent bias in emotion processing—is an important aspect of major depressive disorder (MDD), and such a bias in facial expression recognition has a significant effect on patients’ social lives. Neuroscience research shows abnormal activity in emotion-processing systems regarding facial expressions in MDD. However, the neural basis of negative bias in facial expression processing has not been explored directly. Methods: Sixteen patients with MDD and twenty-three healthy controls (HC) who underwent an fMRI scan during an explicit facial emotion task with happy to sad faces were selected. We identified brain areas in which the MDD and HC groups showed different correlations between the behavioral negative bias scores and functional activities. Results: Behavioral data confirmed the existence of a higher negative bias in the MDD group. Regarding the relationship with neural activity, higher activity of happy faces in the posterior cerebellum was related to a higher negative bias in the MDD group, but lower negative bias in the HC group. Limitations: The sample size was small, and the possible effects of medication were not controlled for in this study. Conclusions: We confirmed a negative bias in the recognition of facial expressions in patients with MDD. fMRI data suggest the cerebellum as a moderator of facial emotion processing, which biases the recognition of facial expressions toward their own mood.

Background: Cluster headache is a rare, strictly unilateral, severe episodic primary headache disorder. Due to the unpredictable and episodic nature of the attacks, nitroglycerin has been used to trigger attacks for research purposes to further our understanding of cluster headache pathophysiology. Objectives: We aimed to identify regions of significant cerebral blood flow (CBF) changes during nitroglycerin triggered cluster headache attacks, using MRI with arterial spin labelling (ASL). Methods: Thirty-three subjects aged 18–60 years with episodic and chronic cluster headache were recruited and attended an open clinical screening visit without scanning to receive an intravenous nitroglycerin infusion (0.5 μg/kg/min over 20 min). Those for whom nitroglycerin successfully triggered a cluster headache attack, were invited to attend two subsequent scanning visits. They received either single-blinded intravenous nitroglycerin (0.5 μg/kg/min) or an equivalent volume of single-blinded intravenous 0.9% sodium chloride over a 20-minute infusion. Whole-brain CBF maps were acquired using a 3 Tesla MRI scanner pre-infusion and post-infusion. As cluster headache is a rare condition and purely unilateral disorder, an analysis strategy to ensure all the image data corresponded to symptomatology in the same hemisphere, without losing coherence across the group, was adopted. This consisted of spatially normalising all CBF maps to a standard symmetric reference template before flipping the images about the anterior-posterior axis for those CBF maps of subjects who experienced their headache in the right hemisphere. This procedure has been employed in previous studies and generated a group data set with expected features on the left hemisphere only. Results: Twenty-two subjects successfully responded to the nitroglycerin infusion and experienced triggered cluster headache attacks. A total of 20 subjects completed the placebo scanning visit, 20 completed the nitroglycerin scanning visit, and 18 subjects had completed both the nitroglycerin and placebo scanning visits. In a whole-brain analysis, we identified regions of significantly elevated CBF in the medial frontal gyrus, superior frontal gyrus, inferior frontal gyrus and cingulate gyrus, ipsilateral to attack side, in CBF maps acquired during cluster headache attack; compared with data from the placebo session. We also identified significantly reduced CBF in the precuneus, cuneus, superior parietal lobe and occipital lobe contralateral to the attack side. Of particular interest to this field of investigation, both the hypothalamus and ipsilateral ventral pons showed higher CBF in a separate region of interest analysis. Conclusion: Our data demonstrate that severe cluster headache leads to significant increases in regional cerebral perfusion, likely to reflect changes in neuronal activity in several regions of the brain, including the hypothalamus and the ventral pons. These data contribute to our understanding of cluster headache pathophysiology; and suggest that non-invasive ASL technology may be valuable in future mechanistic studies of this debilitating condition.