MRI-Based topographic parcellation of human cerebral white matter and nuclei II. Rationale and applications with systematics of cerebral connectivity

Acute right opercular stroke-associated polyopic heautoscopy and hallucinations caused by disconnection to the inferior parietal lobule through the superior longitudinal fasciculus III: A single case study

Illusory neuropsychiatric symptoms such as hallucinations or the feeling of a presence (FOP) can occur in diffuse brain lesion or dysfunction, in psychiatric diseases as well as in healthy individuals. Their occurrence due to focal brain lesions is rare, most probably due to underreporting, which limits progress in understanding their underlying mechanisms and anatomical determinants. In this single case study, an 86-year-old patient experienced, in the context of an acute right central opercular ischemic stroke, visual hallucinatory symptoms (including palinopsia), differently lateralized auditory hallucinations and FOP. This unusual clinical constellation could be precisely documented and illustrated while still present, allowing a realistic and immersive visual experience validated by the patient. The acute stroke appeared to be their most plausible cause (after exclusion of other etiologies). Furthermore, accurate analysis of tractographic data suggested that disruption in the posterior bundle of the superior longitudinal fasciculus connecting the stroke lesion to the inferior parietal lobule was the anatomical substrate explaining the FOP and, indirectly, also hallucinations through whiter matter involvement, in coherence with existing literature. We could finally elaborate on symptoms taxonomy and phenomenology (e.g., polyopic heautoscopy, hallucinatory FOP, etc), and on patient's remarkable distancing from them (with some therapeutic implications supported by plausibly engaged mechanisms). This case not only authentically enriched the description of such rare combination of heterogenous illusory symptoms through this novel visualization-based reporting approach, but disclosed an unrevealed anatomo-clinical link relating all of them to the acute stroke lesion through an association fiber, thereby contributing to the understanding of these intriguing symptoms and their determinants.

Harmonized diffusion MRI data and white matter measures from the Adolescent Brain Cognitive Development Study

The Adolescent Brain Cognitive Development (ABCD) Study® has collected data from over 10,000 children across 21 sites, providing insights into adolescent brain development. However, site-specific scanner variability has made it challenging to use diffusion MRI (dMRI) data from this study. To address this, a dataset of harmonized and processed ABCD dMRI data (from release 3) has been created, comprising quality-controlled imaging data from 9,345 subjects, focusing exclusively on the baseline session, i.e., the first time point of the study. This resource required substantial computational time (approx. 50,000 CPU hours) for harmonization, whole-brain tractography, and white matter parcellation. The dataset includes harmonized dMRI data, 800 white matter clusters, 73 anatomically labeled white matter tracts in full and low resolution, and 804 different dMRI-derived measures per subject (72.3 TB total size). Accessible via the NIMH Data Archive, it offers a large-scale dMRI dataset for studying structural connectivity in child and adolescent neurodevelopment. Additionally, several post-harmonization experiments were conducted to demonstrate the success of the harmonization process on the ABCD dataset.

A proposed structural connectivity matrices approach for the superior fronto-occipital fascicle in the Harvard-Oxford Atlas comparative framework following the Pandya comparative extrapolation principle

A key set of connections necessary for the most complex brain functions are the long association cortico-cortical fiber tracts. These pathways have been described by the Dejerines and others using post mortem histological or brain dissection techniques. Given methodological limitations, these fiber connections have not been delineated completely in humans. Although the stem portions of fiber tracts have been identified in humans, their precise origins and terminations remain to be determined. By contrast, the origins and terminations as well as the stems of long cortico-cortical association fiber pathways in monkeys have been detailed in the macaque monkey brain using experimental tract tracing methods. Deepak Pandya made major contributions to the delineation of fiber tracts in the monkey brain. In the early 1990s, he compared his observations in monkeys with the original descriptions in humans by the Dejerines. With the advent of diffusion-weighted imaging, Dr. Pandya extended this line of investigation to the human brain with Dr. Nikos Makris. In this translational analysis of long association cortico-cortical fiber tracts, they applied a principle of extrapolation from monkey to human. In the present study, we addressed the reasoning and the complex methodology in translating brain structural connectivity from monkey to human in one cortico-cortical fiber tract, namely the superior fronto-occipital fascicle, which was delineated in both species by Dr. Pandya and colleagues. Furthermore, we represented this information in the form of connectional matrices in the context of the HOA2.0-ComPaRe framework, a homological monkey-to-human translational system used in neuroimaging.

Breakdown of category-specific word representations in a brain-constrained neurocomputational model of semantic dementia

The neurobiological nature of semantic knowledge, i.e., the encoding and storage of conceptual information in the human brain, remains a poorly understood and hotly debated subject. Clinical data on semantic deficits and neuroimaging evidence from healthy individuals have suggested multiple cortical regions to be involved in the processing of meaning. These include semantic hubs (most notably, anterior temporal lobe, ATL) that take part in semantic processing in general as well as sensorimotor areas that process specific aspects/categories according to their modality. Biologically inspired neurocomputational models can help elucidate the exact roles of these regions in the functioning of the semantic system and, importantly, in its breakdown in neurological deficits. We used a neuroanatomically constrained computational model of frontotemporal cortices implicated in word acquisition and processing, and adapted it to simulate and explain the effects of semantic dementia (SD) on word processing abilities. SD is a devastating, yet insufficiently understood progressive neurodegenerative disease, characterised by semantic knowledge deterioration that is hypothesised to be specifically related to neural damage in the ATL. The behaviour of our brain-based model is in full accordance with clinical data-namely, word comprehension performance decreases as SD lesions in ATL progress, whereas word repetition abilities remain less affected. Furthermore, our model makes predictions about lesion- and category-specific effects of SD: our simulation results indicate that word processing should be more impaired for object- than for action-related words, and that degradation of white matter should produce more severe consequences than the same proportion of grey matter decay. In sum, the present results provide a neuromechanistic explanatory account of cortical-level language impairments observed during the onset and progress of semantic dementia.

The Relationship Between Cortical Morphological and Functional Topological Properties and Clinical Manifestations in Patients with Posttraumatic Diffuse Axonal Injury: An Individual Brain Network Study

To evaluate the altered network topological properties and their clinical relevance in patients with posttraumatic diffuse axonal injury (DAI). Forty-seven participants were recruited in this study, underwent 3D T1-weighted and resting-state functional MRI, and had single-subject morphological brain networks (MBNs) constructed by Kullback-Leibler divergence and functional brain networks (FBNs) constructed by Pearson correlation measurement interregional similarity. The global and regional properties were analyzed and compared using graph theory and network-based statistics (NBS), and the relationship with clinical manifestations was assessed. Compared with those of the healthy subjects, MBNs of patients with DAI showed a higher path length ([Formula: see text]: P = 0.021, [Formula: see text]: P = 0.011), lower clustering ([Formula: see text]: P = 0.002) and less small-worldness ([Formula: see text]: P = 0.002), but there was no significant difference in the global properties of FBNs (P: 0.161-0.216). For nodal properties of MBNs and FBNs, several regions showed significant differences between patients with DAI and healthy controls (HCs) (P < 0.05, FDR corrected). NBS analysis revealed that MBNs have more altered morphological connections in the frontal parietal control network and interhemispheric connections (P < 0.05). DAI-related global or nodal properties of MBNs were correlated with physical disability or dyscognition (P < 0.05/7, with Bonferroni correction), and the alteration of functional topology properties mediates this relationship. Our results suggested that disrupted morphological topology properties, which are mediated by FBNs and correlated with clinical manifestations of DAI, play a critical role in the short-term and medium-term phases after trauma.

White matter predicts tDCS antidepressant effects in a sham-controlled clinical trial study

Transcranial direct current stimulation (tDCS) has been used as treatment for depression, but its effects are heterogeneous. We investigated, in a subsample of the clinical trial Escitalopram versus Electrical Direct Current Therapy for Depression Study (ELECTTDCS), whether white matter areas associated with depression disorder were associated with tDCS response. Baseline diffusion tensor imaging data were analyzed from 49 patients (34 females, mean age 41.9) randomized to escitalopram 20 mg/day, tDCS (2 mA, 30 min, 22 sessions), or placebo. Antidepressant outcomes were assessed by Hamilton Depression Rating Scale-17 (HDRS) after 10-week treatment. We used whole-brain tractography for extracting white matter measures for anterior corpus callosum, and bilaterally for cingulum bundle, striato-frontal, inferior occipito-frontal fasciculus and uncinate. For the rostral body, tDCS group showed higher MD associated with antidepressant effects (estimate = -5.13 ± 1.64, p = 0.002), and tDCS significantly differed from the placebo and the escitalopram group. The left striato-frontal tract showed higher FA associated with antidepressant effects (estimate = -2.14 ± 0.72, p = 0.003), and tDCS differed only from the placebo group. For the right uncinate, the tDCS group lower AD values were associated with higher HDRS decrease (estimate = -1.45 ± 0.67, p = 0.031). Abnormalities in white matter MDD-related areas are associated with tDCS antidepressant effects. Suggested better white matter microstructure of the left prefrontal cortex was associated with tDCS antidepressant effects. Future studies should investigate whether these findings are driven by electric field diffusion and density in these areas.

Altered Metabolic Networks in Mesial Temporal Lobe Epilepsy with Focal to Bilateral Seizures

This study was designed to identify whether the metabolic network changes in mesial temporal lobe epilepsy (MTLE) patients with focal to bilateral tonic-clonic seizures (FBTCS) differ from changes in patients without FBTCS. This retrospective analysis enrolled 30 healthy controls and 54 total MTLE patients, of whom 27 had FBTCS. Fluorodeoxyglucose positron emission tomography (FDG-PET) data and graph theoretical analyses were used to examine metabolic connectivity. The differences in metabolic networks between the three groups were compared. Significant changes in both local and global network topology were evident in FBTCS+ patients as compared to healthy controls, with a lower assortative coefficient and altered betweenness centrality in 15 brain regions. While global network measures did not differ significantly when comparing FBTCS- patients to healthy controls, alterations in betweenness centrality were evident in 13 brain regions. Significantly altered betweenness centrality was also observed in four brain regions when comparing patients with and without FBTCS. The study revealed greater metabolic network abnormalities in MTLE patients with FBTCS as compared to FBTCS- patients, indicating the existence of distinct epileptogenic networks. These findings can provide insight into the pathophysiological basis of FBTCS.

Three-Dimensional Digital Reconstruction of the Cerebellar Cortex: Lobule Thickness, Surface Area Measurements, and Layer Architecture

The cerebellum is ontogenetically one of the first structures to develop in the central nervous system; nevertheless, it has been only recently reconsidered for its significant neurobiological, functional, and clinical relevance in humans. Thus, it has been a relatively under-studied compared to the cerebrum. Currently, non-invasive imaging modalities can barely reach the necessary resolution to unfold its entire, convoluted surface, while only histological analyses can reveal local information at the micrometer scale. Herein, we used the BigBrain dataset to generate area and point-wise thickness measurements for all layers of the cerebellar cortex and for each lobule in particular. We found that the overall surface area of the cerebellar granular layer (including Purkinje cells) was 1,732 cm2 and the molecular layer was 1,945 cm2. The average thickness of the granular layer is 0.88 mm (± 0.83) and that of the molecular layer is 0.32 mm (± 0.08). The cerebellum (both granular and molecular layers) is thicker at the depth of the sulci and thinner at the crowns of the gyri. Globally, the granular layer is thicker in the lateral-posterior-inferior region than the medial-superior regions. The characterization of individual layers in the cerebellum achieved herein represents a stepping-stone for investigations interrelating structural and functional connectivity with cerebellar architectonics using neuroimaging, which is a matter of considerable relevance in basic and clinical neuroscience. Furthermore, these data provide templates for the construction of cerebellar topographic maps and the precise localization of structural and functional alterations in diseases affecting the cerebellum.

Investigating cognitive neuroscience theories of human intelligence: A connectome-based predictive modeling approach

Central to modern neuroscientific theories of human intelligence is the notion that general intelligence depends on a primary brain region or network, engaging spatially localized (rather than global) neural representations. Recent findings in network neuroscience, however, challenge this assumption, providing evidence that general intelligence may depend on system-wide network mechanisms, suggesting that local representations are necessary but not sufficient to account for the neural architecture of human intelligence. Despite the importance of this key theoretical distinction, prior research has not systematically investigated the role of local versus global neural representations in predicting general intelligence. We conducted a large-scale connectome-based predictive modeling study (N = 297), administering resting-state fMRI and a comprehensive cognitive battery to evaluate the efficacy of modern neuroscientific theories of human intelligence, including spatially localized theories (Lateral Prefrontal Cortex Theory, Parieto-Frontal Integration Theory, and Multiple Demand Theory) and recent global accounts (Process Overlap Theory and Network Neuroscience Theory). The results of our study demonstrate that general intelligence can be predicted by local functional connectivity profiles but is most robustly explained by global profiles of whole-brain connectivity. Our findings further suggest that the improved efficacy of global theories is not reducible to a greater strength or number of connections, but instead results from considering both strong and weak connections that provide the basis for intelligence (as predicted by the Network Neuroscience Theory). Our results highlight the importance of considering local neural representations in the context of a global information-processing architecture, suggesting future directions for theory-driven research on system-wide network mechanisms underlying general intelligence.

Late dominance of the right hemisphere during narrative comprehension

PET and fMRI studies suggest that auditory narrative comprehension is supported by a bilateral multilobar cortical network. The superior temporal resolution of magnetoencephalography (MEG) makes it an attractive tool to investigate the dynamics of how different neuroanatomic substrates engage during narrative comprehension. Using beta-band power changes as a marker of cortical engagement, we studied MEG responses during an auditory story comprehension task in 31 healthy adults. The protocol consisted of two runs, each interleaving 7 blocks of the story comprehension task with 15 blocks of an auditorily presented math task as a control for phonological processing, working memory, and attention processes. Sources at the cortical surface were estimated with a frequency-resolved beamformer. Beta-band power was estimated in the frequency range of 16-24 Hz over 1-sec epochs starting from 400 msec after stimulus onset until the end of a story or math problem presentation. These power estimates were compared to 1-second epochs of data before the stimulus block onset. The task-related cortical engagement was inferred from beta-band power decrements. Group-level source activations were statistically compared using non-parametric permutation testing. A story-math contrast of beta-band power changes showed greater bilateral cortical engagement within the fusiform gyrus, inferior and middle temporal gyri, parahippocampal gyrus, and left inferior frontal gyrus (IFG) during story comprehension. A math-story contrast of beta power decrements showed greater bilateral but left-lateralized engagement of the middle frontal gyrus and superior parietal lobule. The evolution of cortical engagement during five temporal windows across the presentation of stories showed significant involvement during the first interval of the narrative of bilateral opercular and insular regions as well as the ventral and lateral temporal cortex, extending more posteriorly on the left and medially on the right. Over time, there continued to be sustained right anterior ventral temporal engagement, with increasing involvement of the right anterior parahippocampal gyrus, STG, MTG, posterior superior temporal sulcus, inferior parietal lobule, frontal operculum, and insula, while left hemisphere engagement decreased. Our findings are consistent with prior imaging studies of narrative comprehension, but in addition, they demonstrate increasing right-lateralized engagement over the course of narratives, suggesting an important role for these right-hemispheric regions in semantic integration as well as social and pragmatic inference processing.

HOA2.0-ComPaRe: A next generation Harvard-Oxford Atlas comparative parcellation reasoning method for human and macaque individual brain parcellation and atlases of the cerebral cortex

Comparative structural neuroanatomy is a cornerstone for understanding human brain structure and function. A parcellation framework that relates systematically to fundamental principles of histological organization is an essential step in generating structural comparisons between species. In the present investigation, we developed a comparative parcellation reasoning system (ComPaRe), which is a formal ontological system in human and non-human primate brains based on the cortical cytoarchitectonic mapping used for both species as detailed by Brodmann. ComPaRe provides a theoretical foundation for mapping neural systems in humans and other species using neuroimaging. Based on this approach, we revised the methodology of the original Harvard-Oxford Atlas (HOA) system of brain parcellation to produce a comparative framework for the human (hHOA) and the rhesus monkey (mHOA) brains, which we refer to as HOA2.0-ComPaRe. In addition, we used dedicated segmentation software in the publicly available 3D Slicer platform to parcellate an individual human and rhesus monkey brain. This method produces quantitative morphometric parcellations in the individual brains. Based on these parcellations we created a representative template and 3D brain atlas for the two species, each based on a single subject. Thus, HOA2.0-ComPaRe provides a theoretical foundation for mapping neural systems in humans and other species using neuroimaging, while also representing a significant revision of the original human and macaque monkey HOA parcellation schemas. The methodology and atlases presented here can be used in basic and clinical neuroimaging for morphometric (volumetric) analysis, further generation of atlases, as well as localization of function and structural lesions.

Alteration of a brain network with stable and strong functional connections in subjects with schizophrenia

It is widely accepted that there are some common network patterns in the human brain. However, the existence of stable and strong functional connections in the human brain and whether they change in schizophrenia is still a question. By setting 1% connections with the smallest coefficient of variation, we found a widespread brain functional network (frame network) in healthy people(n = 380, two datasets from public databases). We then explored the alterations in a medicated group (60 subjects with schizophrenia vs 71 matched controls) and a drug-naive first-episode group (68 subjects with schizophrenia vs 45 matched controls). A linear support vector classifier (SVC) was constructed to distinguish patients and controls using the medicated patients' frame network. We found most frame connections of healthy people had high strength, which were symmetrical and connected the left and right hemispheres. Conversely, significant differences in frame connections were observed in both patient groups, which were positively correlated with negative symptoms (mainly language dysfunction). Additionally, patients' frame network were more left-lateralized, concentrating on the left frontal lobe, and was quite accurate at distinguishing medicated patients from controls (classifier accuracy was 78.63%, sensitivity was 86.67%, specificity was 76.06%, and the area under the curve (AUC) was 0.83). Furthermore, the results were repeated in the drug-naive set (accuracy was 84.96%, sensitivity was 85.29%, specificity was 88.89%, and AUC was 0.93). These findings indicate that the abnormal pattern of frame network in subjects with schizophrenia might provide new insights into the dysconnectivity in schizophrenia.

In Vivo 7-Tesla MRI Investigation of Brain Iron and Its Metabolic Correlates in Chronic Schizophrenia

Brain iron is central to dopaminergic neurotransmission, a key component in schizophrenia pathology. Iron can also generate oxidative stress, which is one proposed mechanism for gray matter volume reduction in schizophrenia. The role of brain iron in schizophrenia and its potential link to oxidative stress has not been previously examined. In this study, we used 7-Tesla MRI quantitative susceptibility mapping (QSM), magnetic resonance spectroscopy (MRS), and structural T₁ imaging in 12 individuals with chronic schizophrenia and 14 healthy age-matched controls. In schizophrenia, there were higher QSM values in bilateral putamen and higher concentrations of phosphocreatine and lactate in caudal anterior cingulate cortex (caCC). Network-based correlation analysis of QSM across corticostriatal pathways as well as the correlation between QSM, MRS, and volume, showed distinct patterns between groups. This study introduces increased iron in the putamen in schizophrenia in addition to network-wide disturbances of iron and metabolic status.

Anatomically curated segmentation of human subcortical structures in high resolution magnetic resonance imaging: An open science approach

Magnetic resonance imaging (MRI)-based brain segmentation has recently been revolutionized by deep learning methods. These methods use large numbers of annotated segmentations to train algorithms that have the potential to perform brain segmentations reliably and quickly. However, training data for these algorithms are frequently obtained from automated brain segmentation systems, which may contain inaccurate neuroanatomy. Thus, the neuroimaging community would benefit from an open source database of high quality, neuroanatomically curated and manually edited MRI brain images, as well as the publicly available tools and detailed procedures for generating these curated data. Manual segmentation approaches are regarded as the gold standard for brain segmentation and parcellation. These approaches underpin the construction of neuroanatomically accurate human brain atlases. In addition, neuroanatomically precise definitions of MRI-based regions of interest (ROIs) derived from manual brain segmentation are essential for accuracy in structural connectivity studies and in surgical planning for procedures such as deep brain stimulation. However, manual segmentation procedures are time and labor intensive, and not practical in studies utilizing very large datasets, large cohorts, or multimodal imaging. Automated segmentation methods were developed to overcome these issues, and provide high data throughput, increased reliability, and multimodal imaging capability. These methods utilize manually labeled brain atlases to automatically parcellate the brain into different ROIs, but do not have the anatomical accuracy of skilled manual segmentation approaches. In the present study, we developed a custom software module for manual editing of brain structures in the freely available 3D Slicer software platform that employs principles and tools based on pioneering work from the Center for Morphometric Analysis (CMA) at Massachusetts General Hospital. We used these novel 3D Slicer segmentation tools and techniques in conjunction with well-established neuroanatomical definitions of subcortical brain structures to manually segment 50 high resolution T1w MRI brains from the Human Connectome Project (HCP) Young Adult database. The structural definitions used herein are associated with specific neuroanatomical ontologies to systematically interrelate histological and MRI-based morphometric definitions. The resulting brain datasets are publicly available and will provide the basis for a larger database of anatomically curated brains as an open science resource.

Reduced functional connectivity supports statistical learning of temporally distributed regularities

Statistical learning is a powerful ability that extracts regularities from our environment and makes predictions about future events. Using functional magnetic resonance imaging, we aimed to probe how a wide range of brain areas are intertwined to support statistical learning, characterising its architecture in the whole-brain functional connectivity (FC). Participants performed a statistical learning task of temporally distributed regularities. We used refined behavioural learning scores to associate individuals' learning performances with the FC changed by statistical learning. As a result, the learning performance was mediated by the activation strength in the lateral occipital cortex, angular gyrus, precuneus, anterior cingulate cortex, and superior frontal gyrus. Through a group independent component analysis, activations of the superior frontal network showed the largest correlation with the statistical learning performances. Seed-to-voxel whole-brain and seed-to-ROI FC analyses revealed that the FC between the superior frontal gyrus and the salience, language, and dorsal attention networks were reduced during statistical learning. We suggest that the weakened functional connections between the superior frontal gyrus and brain regions involved in top-down control processes serve a pivotal role in statistical learning, supporting better processing of novel information such as the extraction of new patterns from the environment.

A hybrid high-resolution anatomical MRI atlas with sub-parcellation of cortical gyri using resting fMRI

We present a new high-quality, single-subject atlas with sub-millimeter voxel resolution, high SNR, and excellent gray-white tissue contrast to resolve fine anatomical details. The atlas is labeled into two parcellation schemes: 1) the anatomical BCI-DNI atlas, which is manually labeled based on known morphological and anatomical features, and 2) the hybrid USCBrain atlas, which incorporates functional information to guide the sub-parcellation of cerebral cortex. In both cases, we provide consistent volumetric and cortical surface-based parcellation and labeling. The intended use of the atlas is as a reference template for structural coregistration and labeling of individual brains. A single-subject T1-weighted image was acquired five times at a resolution of 0.547 mm × 0.547 mm × 0.800 mm and averaged. Images were processed by an expert neuroanatomist using semi-automated methods in BrainSuite to extract the brain, classify tissue-types, and render anatomical surfaces. Sixty-six cortical and 29 noncortical regions were manually labeled to generate the BCI-DNI atlas. The cortical regions were further sub-parcellated into 130 cortical regions based on multi-subject connectivity analysis using resting fMRI (rfMRI) data from the Human Connectome Project (HCP) database to produce the USCBrain atlas. In addition, we provide a delineation between sulcal valleys and gyral crowns, which offer an additional set of 26 sulcal subregions per hemisphere. Lastly, a probabilistic map is provided to give users a quantitative measure of reliability for each gyral subdivision. Utility of the atlas was assessed by computing Adjusted Rand Indices (ARIs) between individual sub-parcellations obtained through structural-only coregistration to the USCBrain atlas and sub-parcellations obtained directly from each subject's resting fMRI data. Both atlas parcellations can be used with the BrainSuite, FreeSurfer, and FSL software packages.

Late combination shows that MEG adds to MRI in classifying MCI versus controls

Early detection of Alzheimer's disease (AD) is essential for developing effective treatments. Neuroimaging techniques like Magnetic Resonance Imaging (MRI) have the potential to detect brain changes before symptoms emerge. Structural MRI can detect atrophy related to AD, but it is possible that functional changes are observed even earlier. We therefore examined the potential of Magnetoencephalography (MEG) to detect differences in functional brain activity in people with Mild Cognitive Impairment (MCI) - a state at risk of early AD. We introduce a framework for multimodal combination to ask whether MEG data from a resting-state provides complementary information beyond structural MRI data in the classification of MCI versus controls. More specifically, we used multi-kernel learning of support vector machines to classify 163 MCI cases versus 144 healthy elderly controls from the BioFIND dataset. When using the covariance of planar gradiometer data in the low Gamma range (30-48 Hz), we found that adding a MEG kernel improved classification accuracy above kernels that captured several potential confounds (e.g., age, education, time-of-day, head motion). However, accuracy using MEG alone (68%) was worse than MRI alone (71%). When simply concatenating (normalized) features from MEG and MRI into one kernel (Early combination), there was no advantage of combining MEG with MRI versus MRI alone. When combining kernels of modality-specific features (Intermediate combination), there was an improvement in multimodal classification to 74%. The biggest multimodal improvement however occurred when we combined kernels from the predictions of modality-specific classifiers (Late combination), which achieved 77% accuracy (a reliable improvement in terms of permutation testing). We also explored other MEG features, such as the variance versus covariance of magnetometer versus planar gradiometer data within each of 6 frequency bands (delta, theta, alpha, beta, low gamma, or high gamma), and found that they generally provided complementary information for classification above MRI. We conclude that MEG can improve on the MRI-based classification of MCI.

Superior Longitudinal Fasciculus: A Review of the Anatomical Descriptions With Functional Correlates

The superior longitudinal fasciculus (SLF) is part of the longitudinal association fiber system, which lays connections between the frontal lobe and other areas of the ipsilateral hemisphere. As a dominant association fiber bundle, it should correspond to a well-defined structure with a clear anatomical definition. However, this is not the case, and a lot of confusion and overlap surrounds this entity. In this review/opinion study, we survey relevant current literature on the topic and try to clarify the definition of SLF in each hemisphere. After a comparison of postmortem dissections and data obtained from diffusion MRI studies, we discuss the specifics of this bundle regarding its anatomical landmarks, differences in lateralization, as well as individual variability. We also discuss the confusion regarding the arcuate fasciculus in relation to the SLF. Finally, we recommend a nomenclature based on the findings exposed in this review and finalize with a discussion on relevant functional correlates of the structure.

Brain conditions mediate the association between aging and happiness

As the population ages, the realization of a long and happy life is becoming an increasingly important issue in many societies. Therefore, it is important to clarify how happiness and the brain change with aging. In this study, which was conducted with 417 healthy adults in Japan, the analysis showed that fractional anisotropy (FA) correlated with happiness, especially in the internal capsule, corona radiata, posterior thalamic radiation, cingulum, and superior longitudinal fasciculus. According to previous neuroscience studies, these regions are involved in emotional regulation. In psychological studies, emotional regulation has been associated with improvement in happiness. Therefore, this study is the first to show that FA mediates the relationship between age and subjective happiness in a way that bridges these different fields.

Large-scale functional connectivity predicts cognitive impairment related to type 2 diabetes mellitus

BACKGROUND

Large-scale functional connectivity (LSFC) patterns in the brain have unique intrinsic characteristics. Abnormal LSFC patterns have been found in patients with dementia, as well as in those with mild cognitive impairment (MCI), and these patterns predicted their cognitive performance. It has been reported that patients with type 2 diabetes mellitus (T2DM) may develop MCI that could progress to dementia. We investigated whether we could adopt LSFC patterns as discriminative features to predict the cognitive function of patients with T2DM, using connectome-based predictive modeling (CPM) and a support vector machine.

AIM

To investigate the utility of LSFC for predicting cognitive impairment related to T2DM more accurately and reliably.

METHODS

Resting-state functional magnetic resonance images were derived from 42 patients with T2DM and 24 healthy controls. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA). Patients with T2DM were divided into two groups, according to the presence (T2DM-C; n = 16) or absence (T2DM-NC; n = 26) of MCI. Brain regions were marked using Harvard Oxford (HOA-112), automated anatomical labeling (AAL-116), and 264-region functional (Power-264) atlases. LSFC biomarkers for predicting MoCA scores were identified using a new CPM technique. Subsequently, we used a support vector machine based on LSFC patterns for among-group differentiation. The area under the receiver operating characteristic curve determined the appearance of the classification.

RESULTS

CPM could predict the MoCA scores in patients with T2DM (Pearson’s correlation coefficient between predicted and actual MoCA scores, r = 0.32, P=0.0066 [HOA-112 atlas]; r = 0.32, P=0.0078 [AAL-116 atlas]; r = 0.42, P=0.0038 [Power-264 atlas]), indicating that LSFC patterns represent cognition-level measures in these patients. Positive (anti-correlated) LSFC networks based on the Power-264 atlas showed the best predictive performance; moreover, we observed new brain regions of interest associated with T2DM-related cognition. The area under the receiver operating characteristic curve values (T2DM-NC group vs. T2DM-C group) were 0.65-0.70, with LSFC matrices based on HOA-112 and Power-264 atlases having the highest value (0.70). Most discriminative and attractive LSFCs were related to the default mode network, limbic system, and basal ganglia.

CONCLUSION

LSFC provides neuroimaging-based information that may be useful in detecting MCI early and accurately in patients with T2DM.

Exposure to Repetitive Head Impacts Is Associated With Corpus Callosum Microstructure and Plasma Total Tau in Former Professional American Football Players

BACKGROUND

Exposure to repetitive head impacts (RHI) is associated with an increased risk of later-life neurobehavioral dysregulation and neurodegenerative disease. The underlying pathomechanisms are largely unknown.

PURPOSE

To investigate whether RHI exposure is associated with later-life corpus callosum (CC) microstructure and whether CC microstructure is associated with plasma total tau and neuropsychological/neuropsychiatric functioning.

STUDY TYPE

Retrospective cohort study.

POPULATION

Seventy-five former professional American football players (age 55.2 ± 8.0 years) with cognitive, behavioral, and mood symptoms.

FIELD STRENGTH/SEQUENCE

Diffusion-weighted echo-planar MRI at 3 T.

ASSESSMENT

Subjects underwent diffusion MRI, venous puncture, neuropsychological testing, and completed self-report measures of neurobehavioral dysregulation. RHI exposure was assessed using the Cumulative Head Impact Index (CHII). Diffusion MRI measures of CC microstructure (i.e., free-water corrected fractional anisotropy (FA), trace, radial diffusivity (RD), and axial diffusivity (AD)) were extracted from seven segments of the CC (CC1-7), using a tractography clustering algorithm. Neuropsychological tests were selected: Trail Making Test Part A (TMT-A) and Part B (TMT-B), Controlled Oral Word Association Test (COWAT), Stroop Interference Test, and the Behavioral Regulation Index (BRI) from the Behavior Rating Inventory of Executive Function, Adult version (BRIEF-A).

STATISTICAL TESTS

Diffusion MRI metrics were tested for associations with RHI exposure, plasma total tau, neuropsychological performance, and neurobehavioral dysregulation using generalized linear models for repeated measures.

RESULTS

RHI exposure was associated with increased AD of CC1 (correlation coefficient (r) = 0.32, P < 0.05) and with increased plasma total tau (r = 0.34, P < 0.05). AD of the anterior CC1 was associated with increased plasma total tau (CC1: r = 0.30, P < 0.05; CC2: r = 0.29, P < 0.05). Higher trace, AD, and RD of CC1 were associated with better performance (P < 0.05) in TMT-A (trace, r = 0.33; AD, r = 0.31; and RD, r = 0.28) and TMT-B (trace, r = 0.31; RD, r = 0.34). Higher FA and AD of CC2 were associated with better performance (P < 0.05) in TMT-A (FA, r = 0.36; AD, r = 0.28), TMT-B (FA, r = 0.36; AD, r = 0.27), COWAT (FA, r = 0.36; AD, r = 0.32), and BRI (AD, r = 0.29).

DATA CONCLUSION

These results suggest an association among RHI exposure, CC microstructure, plasma total tau, and clinical functioning in former professional American football players.

LEVEL OF EVIDENCE

3 Technical Efficacy Stage: 1.

Brain, behavioral, affective, and sex correlates of recovery from alcohol use disorders

BACKGROUND

Recovery from alcohol use disorders (AUDs) consists of salutary changes in behavior and affect. While evidence suggests that recovery-related behavioral changes, such as abstinence, emerge in tandem with both neural and affective changes, the precise relationships among these changes are unknown. To understand these relationships, we examined associations between the duration of abstinence (DOA), affective states, and neuroimaging-based structural measures of the brain reward system (BRS) in AUD men (AUD_M ) and AUD women (AUD_W ).

METHODS

Participants were community respondents from the Boston area comprising right-handed abstinent individuals with AUD (n = 60; 30 men) and controls without AUD (NC; n = 60; 29 men). Multivariate linear regressions compared short-/mid-term abstainers (≤5 years), long-term abstainers (>5 years), and the NC group on measures of BRS volume (3T magnetic resonance imaging scans) and measures of affect (Profile of Mood States [POMS]; Multiple Affect Adjective Check List [MAACL]; Hamilton Rating Scale for Depression [HRSD]). Analyses contrasted sex differences and accounted for age, education, drinking severity, and verbal IQ.

RESULTS

Compared to the NC group, short-/mid-term abstainers exhibited larger posterior insular volume (total (β = 0.019, 95% CI: 0.004, 0.034)), higher negative affect (POMS Mood Disturbance (β = 27.8, 95% CI: 11.56, 44.04), and lower positive affect (POMS Vigor (β = -4.89, 95% CI: -9.06, -0.72)). Compared to the NC group, Long-term abstainers exhibited significantly smaller volumes of aggregate anterior cingulate cortex (β = -0.06, 95% CI: -0.113, -0.008) and higher HRSD scores (β = 1.56, 95% CI: 0.14, 2.98). Relative to AUD_M , AUD_W exhibited significantly larger right anterior insular volumes (β = 0.03, 95% CI: 0.01, 0.06) and significantly greater MAACL Positive Affect scores (β = 7.56, 95% CI: 0.59, 11.55) in association with DOA.

CONCLUSIONS

We found that differences in abstinence from alcohol were correlated with differences in both neural recovery and affective dimensions of recovery from AUDs. The observed sex differences extend evidence of dimorphic effects of AUDs and recovery on brain structure and function. Future longitudinal research will test inferences concerning the directionality of these relationships.

Community Violence Exposure is Associated with Hippocampus-Insula Resting State Functional Connectivity in Urban Youth

Our previous work has linked childhood violence exposure in Black youth to functional changes in the hippocampus, a brain region sensitive to stress. However, different contexts of violence exposure (e.g., community, home, school) may have differential effects on circuitry. We investigated the unique effect of community violence in predicting resting-state functional connectivity (rsFC) in the hippocampus. Fifty-two (26F) violence-exposed Black youth ages 8-15 performed resting-state functional neuroimaging scans while looking at a fixation cross for seven minutes with eyes open. Seed-based analyses were conducted to examine the association between total violence exposure and rsFC of the hippocampus to the whole brain. Follow-up hierarchical regression analysis were performed to specifically investigate community violence. Violence exposure was associated with higher hippocampus rsFC with a core node of the Default Mode Network (i.e., posterior cingulate cortex) and lower hippocampal rsFC with a core node of the Salience Network (i.e., insula). Community violence uniquely associated with lower hippocampus-insula rsFC, after controlling for home and school violence, sex and age. Age-related decreases in hippocampus-insula rsFC were also present in youth with lower violence exposure, but not in youth with higher violence exposure. This is one of the first studies to investigate the unique impact of community violence, above home and school violence, on threat circuitry. Our data suggest functional alterations in the hippocampus in violence-exposed youth, and that violence in the community may be a more salient form of threat exposure compared to other forms of violence experienced by youth.

Multimodal Investigations of Reward Circuitry and Anhedonia in Adolescent Depression

Depression is a highly prevalent condition with devastating personal and public health consequences that often first manifests during adolescence. Though extensively studied, the pathogenesis of depression remains poorly understood, and efforts to stratify risks and identify optimal interventions have proceeded slowly. A major impediment has been the reliance on an all-or-nothing categorical diagnostic scheme based solely on whether a patient endorses an arbitrary number of common symptoms for a sufficiently long period. This approach masks the well-documented heterogeneity of depression, a disorder that is highly variable in presentation, severity, and course between individuals and is frequently comorbid with other psychiatric conditions. In this targeted review, we outline the limitations of traditional diagnosis-based research and instead advocate an alternative approach centered around symptoms as unique dimensions of clinical dysfunction that span across disorders and more closely reflect underlying neurobiological abnormalities. In particular, we highlight anhedonia-the reduced ability to anticipate and experience pleasure-as a specific, quantifiable index of reward dysfunction and an ideal candidate for dimensional investigation. Anhedonia is a core symptom of depression but also a salient feature of numerous other conditions, and its severity varies widely within clinical and even healthy populations. Similarly, reward dysfunction is a hallmark of depression but is evident across many psychiatric conditions. Reward function is especially relevant in adolescence, a period characterized by exaggerated reward-seeking behaviors and rapid maturation of neural reward circuitry. We detail extensive work by our research group and others to investigate the neural and systemic factors contributing to reward dysfunction in youth, including our cumulative findings using multiple neuroimaging and immunological measures to study depressed adolescents but also trans-diagnostic cohorts with diverse psychiatric symptoms. We describe convergent evidence that reward dysfunction: (a) predicts worse clinical outcomes, (b) is associated with functional and chemical abnormalities within and beyond the neural reward circuitry, (c) is linked to elevated peripheral levels of inflammatory biomarkers, and (d) manifests early in the course of illness. Emphasis is placed on high-resolution neuroimaging techniques, comprehensive immunological assays, and data-driven analyses to fully capture and characterize the complex, interconnected nature of these systems and their contributions to adolescent reward dysfunction.

MRI-based Parcellation and Morphometry of the Individual Rhesus Monkey Brain: the macaque Harvard-Oxford Atlas (mHOA), a translational system referencing a standardized ontology

Investigations of the rhesus monkey (Macaca mulatta) brain have shed light on the function and organization of the primate brain at a scale and resolution not yet possible in humans. A cornerstone of the linkage between non-human primate and human studies of the brain is magnetic resonance imaging, which allows for an association to be made between the detailed structural and physiological analysis of the non-human primate and that of the human brain. To further this end, we present a novel parcellation method and system for the rhesus monkey brain, referred to as the macaque Harvard-Oxford Atlas (mHOA), which is based on the human Harvard-Oxford Atlas (HOA) and grounded in an ontological and taxonomic framework. Consistent anatomical features were used to delimit and parcellate brain regions in the macaque, which were then categorized according to functional systems. This system of parcellation will be expanded with advances in technology and, like the HOA, will provide a framework upon which the results from other experimental studies (e.g., functional magnetic resonance imaging (fMRI), physiology, connectivity, graph theory) can be interpreted.

Extreme capsule is a bottleneck for ventral pathway

As neuroscience literature suggests, extreme capsule is considered a whiter matter tract. Nevertheless, it is not clear whether extreme capsule itself is an association fiber pathway or only a bottleneck for other association fibers to pass. Via our review, investigating anatomical position, connectivity and cognitive role of the bundles in extreme capsule, and by analyzing data from the dissection, it can be argued that extreme capsule is probably a bottleneck for the passage of uncinated fasciculus (UF) and inferior fronto-occipital fasciculus (IFOF), and these fasciculi are responsible for the respective roles in language processing.

Sensory Modulation Disorder and its Neural Circuitry in Adults with ADHD: A Pilot Study

Compared to healthy controls (HCs), individuals with attention-deficit/hyperactivity disorder (ADHD) exhibit more symptoms of sensory processing disorder (SPD), which is associated with difficulties in educational and social activities. Most studies examining comorbid SPD-ADHD have been conducted with children and have not explored relations to brain volumes. In this pilot study, we assessed a subtype of SPD, sensory modulation disorder (SMD), and its relation to select brain volumes in adults with ADHD. We administered part of the Sensory Processing 3-Dimensions Scale (SP3D) to assess subtypes of SMD and collected structural imaging scans from 25 adults with ADHD and 29 healthy controls (HCs). Relative to HCs, subjects with ADHD scored higher on sensory craving (SC) and sensory under-responsivity (SUR) subscales. Although sensory over-responsivity (SOR) was marginally higher, this was no longer true when accounting for co-occurring anxiety. In individuals with ADHD, both SC and SUR were positively associated with amygdalar volume, SUR was also positively associated with striatal volume, whereas SOR was negatively associated with posterior ventral diencephalon volume. These preliminary findings suggest that SC and SUR may be characteristic of ADHD while SOR may be driven by co-occurring anxiety. Because different modalities were associated with different brain volumes, our findings also suggest that the modalities may involve unique neural circuits, but with a partial overlap between SC and SUR. These pilot data provide support for conducting studies examining SMD in larger samples of adults with ADHD to determine reproducibility, applicability and implications of these findings.

Functional connectome organization predicts conversion to psychosis in clinical high-risk youth from the SHARP program

The emergence of prodromal symptoms of schizophrenia and their evolution into overt psychosis may stem from an aberrant functional reorganization of the brain during adolescence. To examine whether abnormalities in connectome organization precede psychosis onset, we performed a functional connectome analysis in a large cohort of medication-naive youth at risk for psychosis from the Shanghai At Risk for Psychosis (SHARP) study. The SHARP program is a longitudinal study of adolescents and young adults at Clinical High Risk (CHR) for psychosis, conducted at the Shanghai Mental Health Center in collaboration with neuroimaging laboratories at Harvard and MIT. Our study involved a total of 251 subjects, including 158 CHRs and 93 age-, sex-, and education-matched healthy controls. During 1-year follow-up, 23 CHRs developed psychosis. CHRs who would go on to develop psychosis were found to show abnormal modular connectome organization at baseline, while CHR non-converters did not. In all CHRs, abnormal modular connectome organization at baseline was associated with a threefold conversion rate. A region-specific analysis showed that brain regions implicated in early-course schizophrenia, including superior temporal gyrus and anterior cingulate cortex, were most abnormal in terms of modular assignment. Our results show that functional changes in brain network organization precede the onset of psychosis and may drive psychosis development in at-risk youth.

Investigation of structure-function correlation among the young offspring of patients with bipolar disorder

Bipolar disorder (BD) has been associated with impaired executive functioning and integrity of fronto-limbic white matter tracts. The evaluation of these factors in young offspring of patients with BD (BDoff) as a high-risk group offers an opportunity to investigate factors that could predict vulnerability to the disorder. This study aims to examine the correlation between neurocognition and neuroimaging findings to evaluate the potential for these findings as biomarkers for the early recognition of BD. We enrolled BDoff (n = 16) who were aged between 12 and 18. Participants were assessed using clinical and neurocognitive tests. In addition, structural brain magnetic resonance and diffusion tensor imaging data were obtained. Mean fractional anisotropy (FA) and mean diffusivity (MD) values of the superior longitudinal fasciculus (SLF) and cingulum were extracted and correlations with neuropsychological data were analyzed. FA values in the SLF were negatively correlated with Stroop interference, the Wisconsin Card Sorting Test, and the Trail Making Test (B-A) scores. MD values in the cingulum were inversely correlated with the Child and Youth Resilience Measure and positively correlated with higher scores on the Barratt Impulsiveness Scale-Attentional. These findings provide a link between features of the brain and cognitive dysfunction in BDoff.

Individual variations of the human corticospinal tract and its hand-related motor fibers using diffusion MRI tractography

The corticospinal tract (CST) is one of the most well studied tracts in human neuroanatomy. Its clinical significance can be demonstrated in many notable traumatic conditions and diseases such as stroke, spinal cord injury (SCI) or amyotrophic lateral sclerosis (ALS). With the advent of diffusion MRI and tractography the computational representation of the human CST in a 3D model became available. However, the representation of the entire CST and, specifically, the hand motor area has remained elusive. In this paper we propose a novel method, using manually drawn ROIs based on robustly identifiable neuroanatomic structures to delineate the entire CST and isolate its hand motor representation as well as to estimate their variability and generate a database of their volume, length and biophysical parameters. Using 37 healthy human subjects we performed a qualitative and quantitative analysis of the CST and the hand-related motor fiber tracts (HMFTs). Finally, we have created variability heat maps from 37 subjects for both the aforementioned tracts, which could be utilized as a reference for future studies with clinical focus to explore neuropathology in both trauma and disease states.

Taking central nervous system regenerative therapies to the clinic: curing rodents versus nonhuman primates versus humans

The central nervous system is known to have limited regenerative capacity. Not only does this halt the human body's reparative processes after central nervous system lesions, but it also impedes the establishment of effective and safe therapeutic options for such patients. Despite the high prevalence of stroke and spinal cord injury in the general population, these conditions remain incurable and place a heavy burden on patients' families and on society more broadly. Neuroregeneration and neural engineering are diverse biomedical fields that attempt reparative treatments, utilizing stem cells-based strategies, biologically active molecules, nanotechnology, exosomes and highly tunable biodegradable systems (e.g., certain hydrogels). Although there are studies demonstrating promising preclinical results, safe clinical translation has not yet been accomplished. A key gap in clinical translation is the absence of an ideal animal or ex vivo model that can perfectly simulate the human microenvironment, and also correspond to all the complex pathophysiological and neuroanatomical factors that affect functional outcomes in humans after central nervous system injury. Such an ideal model does not currently exist, but it seems that the nonhuman primate model is uniquely qualified for this role, given its close resemblance to humans. This review considers some regenerative therapies for central nervous system repair that hold promise for future clinical translation. In addition, it attempts to uncover some of the main reasons why clinical translation might fail without the implementation of nonhuman primate models in the research pipeline.

Mapping the human middle longitudinal fasciculus through a focused anatomo-imaging study: shifting the paradigm of its segmentation and connectivity pattern

Τhe middle longitudinal fasciculus (MdLF) was initially identified in humans as a discrete subcortical pathway connecting the superior temporal gyrus (STG) to the angular gyrus (AG). Further anatomo-imaging studies, however, proposed more sophisticated but conflicting connectivity patterns and have created a vague perception on its functional anatomy. Our aim was, therefore, to investigate the ambiguous structural architecture of this tract through focused cadaveric dissections augmented by a tailored DTI protocol in healthy participants from the Human Connectome dataset. Three segments and connectivity patterns were consistently recorded: the MdLF-I, connecting the dorsolateral Temporal Pole (TP) and STG to the Superior Parietal Lobule/Precuneus, through the Heschl's gyrus; the MdLF-II, connecting the dorsolateral TP and the STG with the Parieto-occipital area through the posterior transverse gyri and the MdLF-III connecting the most anterior part of the TP to the posterior border of the occipital lobe through the AG. The lack of an established termination pattern to the AG and the fact that no significant leftward asymmetry is disclosed tend to shift the paradigm away from language function. Conversely, the theory of "where" and "what" auditory pathways, the essential relationship of the MdLF with the auditory cortex and the functional role of the cortical areas implicated in its connectivity tend to shift the paradigm towards auditory function. Allegedly, the MdLF-I and MdLF-II segments could underpin the perception of auditory representations; whereas, the MdLF-III could potentially subserve the integration of auditory and visual information.

Neuropsychiatric and Behavioral Involvement in AAS Abusers. A Literature Review

Background and Objectives: Anabolic androgenic steroids (AASs) are a complex group of molecules that include both steroidal androgens and synthetic compounds, derived from testosterone. AASs are commonly used to support pharmacological therapy in cases of primary or secondary hypogonadism, major burns, and neoplastic cachexia. Their prolonged and supra-physiological consumption can provoke several adverse effects on various organs and systems. Among these, the physiopathological mechanisms that induce neuropsychiatric disorders related to AAS abuse are poorly known. For this reason, the proposed review aims to retrace the pathway of action of testosterone to focus on the effects on the central nervous system and specifically highlight the effects of AASs on neuropsychiatric and behavioral functions, as well as on lifestyle. Materials and Methods: This review was conducted using PubMed and Google Scholar databases. On these database websites, we searched for articles from 1 January 1980 to March 2019 using the key terms: "AAS," "Anabolic Androgenic Steroids," "brain," and "neurology." Results: The use of AASs through self-administration yields circulating androgens levels, inducing neuron apoptosis, which is linked to thinner cortex and, in general, less cortical volume. The same alterations affect the putamen. These differences were more evident when correlated with longer use. From a functional point of view, prolonged AAS consumption seemed to be related to lower connectivity between amygdala and frontal, striatal, limbic, hippocampal and visual cortical areas. On the other hand, AAS use seems to negatively condition the positive effects of the sport exercise, reducing its important anti-apoptotic and pro-proliferative functions on the hippocampus, implicated in anxiolytic control. Conclusion: This review clarifies the major aspects of the side effects related to AAS use/abuse highlighting the complex mechanisms on neuropsychiatric and cognitive pathological alterations and also the emotional and behavioral dysfunctions.

Metabolic Brain Network Analysis of FDG-PET in Alzheimer's Disease Using Kernel-Based Persistent Features

Recent research of persistent homology in algebraic topology has shown that the altered network organization of human brain provides a promising indicator of many neuropsychiatric disorders and neurodegenerative diseases. However, the current slope-based approach may not accurately characterize changes of persistent features over graph filtration because such curves are not strictly linear. Moreover, our previous integrated persistent feature (IPF) works well on an rs-fMRI cohort while it has not yet been studied on metabolic brain networks. To address these issues, we propose a novel univariate network measurement, kernel-based IPF (KBI), based on the prior IPF, to quantify the difference between IPF curves. In our experiments, we apply the KBI index to study fluorodeoxyglucose positron emission tomography (FDG-PET) imaging data from 140 subjects with Alzheimer's disease (AD), 280 subjects with mild cognitive impairment (MCI), and 280 healthy normal controls (NC). The results show the disruption of network integration in the progress of AD. Compared to previous persistent homology-based measures, as well as other standard graph-based measures that characterize small-world organization and modular structure, our proposed network index KBI possesses more significant group difference and better classification performance, suggesting that it may be used as an effective preclinical AD imaging biomarker.

Dynamic Reorganization of Functional Connectivity Reveals Abnormal Temporal Efficiency in Schizophrenia

Emerging evidence suggests that schizophrenia is associated with brain dysconnectivity. Nonetheless, the implicit assumption of stationary functional connectivity (FC) adopted in most previous resting-state functional magnetic resonance imaging (fMRI) studies raises an open question of schizophrenia-related aberrations in dynamic properties of resting-state FC. This study introduces an empirical method to examine the dynamic functional dysconnectivity in patients with schizophrenia. Temporal brain networks were estimated from resting-state fMRI of 2 independent datasets (patients/controls = 18/19 and 53/57 for self-recorded dataset and a publicly available replication dataset, respectively) by the correlation of sliding time-windowed time courses among regions of a predefined atlas. Through the newly introduced temporal efficiency approach and temporal random network models, we examined, for the first time, the 3D spatiotemporal architecture of the temporal brain network. We found that although prominent temporal small-world properties were revealed in both groups, temporal brain networks of patients with schizophrenia in both datasets showed a significantly higher temporal global efficiency, which cannot be simply attributable to head motion and sampling error. Specifically, we found localized changes of temporal nodal properties in the left frontal, right medial parietal, and subcortical areas that were associated with clinical features of schizophrenia. Our findings demonstrate that altered dynamic FC may underlie abnormal brain function and clinical symptoms observed in schizophrenia. Moreover, we provide new evidence to extend the dysconnectivity hypothesis in schizophrenia from static to dynamic brain network and highlight the potential of aberrant brain dynamic FC in unraveling the pathophysiologic mechanisms of the disease.

A concise and persistent feature to study brain resting-state network dynamics: Findings from the Alzheimer's Disease Neuroimaging Initiative

Alzheimer's disease (AD) is the most common type of dementia in the elderly with no effective treatment currently. Recent studies of noninvasive neuroimaging, resting-state functional magnetic resonance imaging (rs-fMRI) with graph theoretical analysis have shown that patients with AD and mild cognitive impairment (MCI) exhibit disrupted topological organization in large-scale brain networks. In previous work, it is a common practice to threshold such networks. However, it is not only difficult to make a principled choice of threshold values, but also worse is the discard of potential important information. To address this issue, we propose a threshold-free feature by integrating a prior persistent homology-based topological feature (the zeroth Betti number) and a newly defined connected component aggregation cost feature to model brain networks over all possible scales. We show that the induced topological feature (Integrated Persistent Feature) follows a monotonically decreasing convergence function and further propose to use its slope as a concise and persistent brain network topological measure. We apply this measure to study rs-fMRI data from the Alzheimer's Disease Neuroimaging Initiative and compare our approach with five other widely used graph measures across five parcellation schemes ranging from 90 to 1,024 region-of-interests. The experimental results demonstrate that the proposed network measure shows more statistical power and stronger robustness in group difference studies in that the absolute values of the proposed measure of AD are lower than MCI and much lower than normal controls, providing empirical evidence for decreased functional integration in AD dementia and MCI.

Widely Impaired White Matter Integrity and Altered Structural Brain Networks in Psychogenic Non-Epileptic Seizures

OBJECTIVE

The underlying neural correlates of psychogenic non-epileptic seizures (PNES) are still unknown and their identification would be helpful for clinicians and patients. This study aimed to reveal details of white matter microstructure and alterations in brain structural networks in patients with PNES by using diffusion tensor imaging (DTI) and graph theoretical connectivity analysis.

METHODS

Seventeen patients with PNES and 26 age- and sex-matched healthy controls were enrolled. All participants underwent DTI on a 3.0-T MRI scanner, and fractional anisotropy (FA) and mean diffusivity (MD) maps were compared by tract-based spatial statistics. Additionally, the structural networks derived from DTI data were analyzed using graph theory and two different parcellation schemes.

RESULTS

Patients with PNES showed widespread decreases in FA and increases in MD, particularly in the deep white matter. In addition, graph theoretical analysis revealed impaired brain networks in PNES, including increased path length, decreased network efficiency, altered nodal topology, and reduced regional connectivity in the right posterior areas.

CONCLUSION

We found widely impaired white matter integrity and impaired brain structural networks in Japanese patients with PNES. These findings contribute to the accumulation of evidence on PNES and may improve understanding of this condition.

Impact of sex and reproductive status on memory circuitry structure and function in early midlife using structural covariance analysis

Research on age-related memory alterations traditionally targets individuals aged ≥65 years. However, recent studies emphasize the importance of early aging processes. We therefore aimed to characterize variation in brain gray matter structure in early midlife as a function of sex and menopausal status. Subjects included 94 women (33 premenopausal, 29 perimenopausal, and 32 postmenopausal) and 99 demographically comparable men from the New England Family Study. Subjects were scanned with a high-resolution T1 sequence on a 3 T whole body scanner. Sex and reproductive-dependent structural differences were evaluated using Box's M test and analysis of covariances (ANCOVAs) for gray matter volumes. Brain regions of interest included dorsolateral prefrontal cortex (DLPFC), inferior parietal lobule (iPAR), anterior cingulate cortex (ACC), hippocampus (HIPP), and parahippocampus. While we observed expected significant sex differences in volume of hippocampus with women of all groups having higher volumes than men relative to cerebrum size, we also found significant differences in the covariance matrices of perimenopausal women compared with postmenopausal women. Associations between ACC and HIPP/iPAR/DLPFC were higher in postmenopausal women and correlated with better memory performance. Findings in this study underscore the importance of sex and reproductive status in early midlife for understanding memory function with aging.

Interactive histogenesis of axonal strata and proliferative zones in the human fetal cerebral wall

Development of the cerebral wall is characterized by partially overlapping histogenetic events. However, little is known with regards to when, where, and how growing axonal pathways interact with progenitor cell lineages in the proliferative zones of the human fetal cerebrum. We analyzed the developmental continuity and spatial distribution of the axonal sagittal strata (SS) and their relationship with proliferative zones in a series of human brains (8-40 post-conceptional weeks; PCW) by comparing histological, histochemical, and immunocytochemical data with magnetic resonance imaging (MRI). Between 8.5 and 11 PCW, thalamocortical fibers from the intermediate zone (IZ) were initially dispersed throughout the subventricular zone (SVZ), while sizeable axonal "invasion" occurred between 12.5 and 15 PCW followed by callosal fibers which "delaminated" the ventricular zone-inner SVZ from the outer SVZ (OSVZ). During midgestation, the SS extensively invaded the OSVZ, separating cell bands, and a new multilaminar axonal-cellular compartment (MACC) was formed. Preterm period reveals increased complexity of the MACC in terms of glial architecture and the thinning of proliferative bands. The addition of associative fibers and the formation of the centrum semiovale separated the SS from the subplate. In vivo MRI of the occipital SS indicates a "triplet" structure of alternating hypointense and hyperintense bands. Our results highlighted the developmental continuity of sagittally oriented "corridors" of projection, commissural and associative fibers, and histogenetic interaction with progenitors, neurons, and glia. Histogenetical changes in the MACC, and consequently, delineation of the SS on MRI, may serve as a relevant indicator of white matter microstructural integrity in the developing brain.

Combining resting state functional MRI with intraoperative cortical stimulation to map the mentalizing network

OBJECTIVE

To infer the face-based mentalizing network from resting-state functional MRI (rsfMRI) using a seed-based correlation analysis with regions of interest identified during intraoperative cortical electrostimulation.

METHODS

We retrospectively included 23 patients in whom cortical electrostimulation induced transient face-based mentalizing impairment during 'awake' craniotomy for resection of a right-sided diffuse low-grade glioma. Positive stimulation sites were recorded and transferred to the patients' preoperative normalized MRI, and then used as seeds for subsequent seed-to-voxel functional connectivity analyses. The analyses, conducted with an uncorrected voxel-level p-value of 0.001 and a false-discovery-rate cluster-level p-value of 0.05, allowed identification of the cortical structures, functionally coupled with the mentalizing-related sites.

RESULTS

Two clusters of responsive stimulations were identified intraoperatively - one in the right dorsolateral prefrontal cortex (dlPFC, n = 13) and the other in the right inferior frontal gyrus (IFG, n = 10). A whole group level analysis revealed that stimulation sites correlated mainly with voxels located in the pars triangularis of the IFG, the dorsolateral and dorsomedial prefrontal cortices, the temporo-parietal junction, the posterior superior temporal sulcus, and the posterior inferior temporal/fusiform gyrus. Other analyses, taking into consideration the location of the responsive sites (IFG versus dlPFC cluster), highlighted only minor differences between both groups.

CONCLUSIONS

The present study successfully demonstrated the involvement of a large-scale neural network in the face-based mentalizing that strongly matches networks, classically identified using task-based fMRI paradigms. We thus validated the combination of rsfMRI and stimulation mapping as a powerful approach to identify functional networks in brain-damaged patients.

Topological analyses of functional connectomics: A crucial role of global signal removal, brain parcellation, and null models

Recently, functional connectome studies based on resting-state functional magnetic resonance imaging (R-fMRI) and graph theory have greatly advanced our understanding of the topological principles of healthy and diseased brains. However, how different strategies for R-fMRI data preprocessing and for connectome analyses jointly affect topological characterization and contrastive research of brain networks remains to be elucidated. Here, we used two R-fMRI data sets, a healthy young adult data set and an Alzheimer's disease (AD) patient data set, and up to 42 analysis strategies to comprehensively investigate the joint influence of three key factors (global signal regression, regional parcellation schemes, and null network models) on the topological analysis and contrastive research of whole-brain functional networks. At the global level, we first found that these three factors affected not only the quantitative values but also the individual variability profile in small-world related metrics and modularity, wherein global signal regression exhibited the predominant influence. Moreover, strategies without global signal regression and with topological randomization null model enhanced the sensitivity of the detection of differences between AD and control groups in small-worldness and modularity. At the nodal level, strategies of global signal regression dominantly influenced the spatial distribution of both hubs and between-group differences in terms of nodal degree centrality. Together, we highlight the remarkable joint influence of global signal regression, regional parcellation schemes and null network models on functional connectome analyses in both health and diseases, which may provide guidance for the choice of analysis strategies in future functional network studies.

White Matter Correlates of Mild Traumatic Brain Injuries in Women Subjected to Intimate-Partner Violence: A Preliminary Study

A large proportion (range of 44-75%) of women who experience intimate-partner violence (IPV) have been shown to sustain repetitive mild traumatic brain injuries (mTBIs) from their abusers. Further, despite requests for research on TBI-related health outcomes, there are currently only a handful of studies addressing this issue and only one prior imaging study that has investigated the neural correlates of IPV-related TBIs. In response, we examined specific regions of white matter microstructure in 20 women with histories of IPV. Subjects were imaged on a 3-Tesla Siemens Magnetom TrioTim scanner using diffusion magnetic resonance imaging. We investigated the association between a score reflecting number and recency of IPV-related mTBIs and fractional anisotropy (FA) in the posterior and superior corona radiata as well as the posterior thalamic radiation, brain regions shown previously to be involved in mTBI. We also investigated the association between several cognitive measures, namely learning, memory, and cognitive flexibility, and FA in the white matter regions of interest. We report a negative correlation between the brain injury score and FA in regions of the posterior and superior corona radiata. We failed to find an association between our cognitive measures and FA in these regions, but the interpretation of these results remains inconclusive due to possible power issues. Overall, these data build upon the small but growing literature demonstrating potential consequences of mTBIs for women experiencing IPV, and further underscore the urgent need for larger and more comprehensive studies in this area.

Fused Lasso Regression for Identifying Differential Correlations in Brain Connectome Graphs

In this paper, we propose a procedure to find differential edges between two graphs from high-dimensional data. We estimate two matrices of partial correlations and their differences by solving a penalized regression problem. We assume sparsity only on differences between two graphs, not graphs themselves. Thus, we impose an ℓ ₂ penalty on partial correlations and an ℓ ₁ penalty on their differences in the penalized regression problem. We apply the proposed procedure to finding differential functional connectivity between healthy individuals and Alzheimer's disease patients.

An anatomically curated fiber clustering white matter atlas for consistent white matter tract parcellation across the lifespan

This work presents an anatomically curated white matter atlas to enable consistent white matter tract parcellation across different populations. Leveraging a well-established computational pipeline for fiber clustering, we create a tract-based white matter atlas including information from 100 subjects. A novel anatomical annotation method is proposed that leverages population-based brain anatomical information and expert neuroanatomical knowledge to annotate and categorize the fiber clusters. A total of 256 white matter structures are annotated in the proposed atlas, which provides one of the most comprehensive tract-based white matter atlases covering the entire brain to date. These structures are composed of 58 deep white matter tracts including major long range association and projection tracts, commissural tracts, and tracts related to the brainstem and cerebellar connections, plus 198 short and medium range superficial fiber clusters organized into 16 categories according to the brain lobes they connect. Potential false positive connections are annotated in the atlas to enable their exclusion from analysis or visualization. In addition, the proposed atlas allows for a whole brain white matter parcellation into 800 fiber clusters to enable whole brain connectivity analyses. The atlas and related computational tools are open-source and publicly available. We evaluate the proposed atlas using a testing dataset of 584 diffusion MRI scans from multiple independently acquired populations, across genders, the lifespan (1 day-82 years), and different health conditions (healthy control, neuropsychiatric disorders, and brain tumor patients). Experimental results show successful white matter parcellation across subjects from different populations acquired on multiple scanners, irrespective of age, gender or disease indications. Over 99% of the fiber tracts annotated in the atlas were detected in all subjects on average. One advantage in terms of robustness is that the tract-based pipeline does not require any cortical or subcortical segmentations, which can have limited success in young children and patients with brain tumors or other structural lesions. We believe this is the first demonstration of consistent automated white matter tract parcellation across the full lifespan from birth to advanced age.

NeuroCave: A web-based immersive visualization platform for exploring connectome datasets

We introduce NeuroCave, a novel immersive visualization system that facilitates the visual inspection of structural and functional connectome datasets. The representation of the human connectome as a graph enables neuroscientists to apply network-theoretic approaches in order to explore its complex characteristics. With NeuroCave, brain researchers can interact with the connectome-either in a standard desktop environment or while wearing portable virtual reality headsets (such as Oculus Rift, Samsung Gear, or Google Daydream VR platforms)-in any coordinate system or topological space, as well as cluster brain regions into different modules on-demand. Furthermore, a default side-by-side layout enables simultaneous, synchronized manipulation in 3D, utilizing modern GPU hardware architecture, and facilitates comparison tasks across different subjects or diagnostic groups or longitudinally within the same subject. Visual clutter is mitigated using a state-of-the-art edge bundling technique and through an interactive layout strategy, while modular structure is optimally positioned in 3D exploiting mathematical properties of platonic solids. NeuroCave provides new functionality to support a range of analysis tasks not available in other visualization software platforms.

Cerebral white matter sex dimorphism in alcoholism: a diffusion tensor imaging study

Excessive alcohol consumption is associated with brain aberrations, including abnormalities in frontal and limbic brain regions. In a prior diffusion tensor magnetic resonance imaging (dMRI) study of neuronal circuitry connecting the frontal lobes and limbic system structures, we demonstrated decreases in white matter fractional anisotropy in abstinent alcoholic men. In the present study, we examined sex differences in alcoholism-related abnormalities of white matter connectivity and their association with alcoholism history. The dMRI scans were acquired from 49 abstinent alcoholic individuals (26 women) and 41 nonalcoholic controls (22 women). Tract-based spatial statistical tools were used to estimate regional FA of white matter tracts and to determine sex differences and their relation to measures of alcoholism history. Sex-related differences in white matter connectivity were observed in association with alcoholism: Compared to nonalcoholic men, alcoholic men had diminished FA in portions of the corpus callosum, the superior longitudinal fasciculi II and III, and the arcuate fasciculus and extreme capsule. In contrast, alcoholic women had higher FA in these regions. Sex differences also were observed for correlations between corpus callosum FA and length of sobriety. Our results suggest that sexual dimorphism in white matter microstructure in abstinent alcoholics may implicate underlying differences in the neurobehavioral liabilities for developing alcohol abuse disorders, or for sequelae following abuse.

Fronto-Parietal Subnetworks Flexibility Compensates For Cognitive Decline Due To Mental Fatigue

Fronto-parietal subnetworks were revealed to compensate for cognitive decline due to mental fatigue by community structure analysis. Here, we investigate changes in topology of subnetworks of resting-state fMRI networks due to mental fatigue induced by prolonged performance of a cognitively demanding task, and their associations with cognitive decline. As it is well established that brain networks have modular organization, community structure analyses can provide valuable information about mesoscale network organization and serve as a bridge between standard fMRI approaches and brain connectomics that quantify the topology of whole brain networks. We developed inter- and intramodule network metrics to quantify topological characteristics of subnetworks, based on our hypothesis that mental fatigue would impact on functional relationships of subnetworks. Functional networks were constructed with wavelet correlation and a data-driven thresholding scheme based on orthogonal minimum spanning trees, which allowed detection of communities with weak connections. A change from pre- to posttask runs was found for the intermodule density between the frontal and the temporal subnetworks. Seven inter- or intramodule network metrics, mostly at the frontal or the parietal subnetworks, showed significant predictive power of individual cognitive decline, while the network metrics for the whole network were less effective in the predictions. Our results suggest that the control-type fronto-parietal networks have a flexible topological architecture to compensate for declining cognitive ability due to mental fatigue. This community structure analysis provides valuable insight into connectivity dynamics under different cognitive states including mental fatigue.

Frontostriatal Dysfunction During Decision Making in Attention-Deficit/Hyperactivity Disorder and Obsessive-Compulsive Disorder

Background

The aim of the current paper is to provide the first comparison of computational mechanisms and neurofunctional substrates in adolescents with attention-deficit/hyperactivity disorder (ADHD) and adolescents with obsessive-compulsive disorder (OCD) during decision making under ambiguity.

Methods

Sixteen boys with ADHD, 20 boys with OCD, and 20 matched control subjects (12–18 years of age) completed a functional magnetic resonance imaging version of the Iowa Gambling Task. Brain activation was compared between groups using three-way analysis of covariance. Hierarchical Bayesian analysis was used to compare computational modeling parameters between groups.

Results

Patient groups shared reduced choice consistency and relied less on reinforcement learning during decision making relative to control subjects, while adolescents with ADHD alone demonstrated increased reward sensitivity. During advantageous choices, both disorders shared underactivation in ventral striatum, while OCD patients showed disorder-specific underactivation in the ventromedial orbitofrontal cortex. During outcome evaluation, shared underactivation to losses in patients relative to control subjects was found in the medial prefrontal cortex and shared underactivation to wins was found in the left putamen/caudate. ADHD boys showed disorder-specific dysfunction in the right putamen/caudate, which was activated more to losses in patients with ADHD but more to wins in control subjects.

Conclusions

The findings suggest shared deficits in using learned reward expectancies to guide decision making, as well as shared dysfunction in medio-fronto-striato-limbic brain regions. However, findings of unique dysfunction in the ventromedial orbitofrontal cortex in OCD and in the right putamen in ADHD indicate additional, disorder-specific abnormalities and extend similar findings from inhibitory control tasks in the disorders to the domain of decision making under ambiguity.