Gray Matter Components Research Articles

Multimodal neuroimaging network associated with executive function in adolescent major depressive disorder patients via cognition-guided magnetic resonance imaging fusion.

Adolescents are high-risk population for major depressive disorder. Executive dysfunction emerges as a common feature of depression and exerts a significant influence on the social functionality of adolescents. This study aimed to identify the multimodal co-varying brain network related to executive function in adolescent with major depressive disorder. A total of 24 adolescent major depressive disorder patients and 43 healthy controls were included and completed the Intra-Extra Dimensional Set Shift Task. Multimodal neuroimaging data, including the amplitude of low-frequency fluctuations from resting-state functional magnetic resonance imaging and gray matter volume from structural magnetic resonance imaging, were combined with executive function using a supervised fusion method named multimodal canonical correlation analysis with reference plus joint independent component analysis. The major depressive disorder showed more total errors than the healthy controls in the Intra-Extra Dimensional Set Shift task. Their performance on the Intra-Extra Dimensional Set Shift Task was negatively related to the 14-item Hamilton Rating Scale for Anxiety score. We discovered an executive function-related multimodal fronto-occipito-temporal network with lower amplitude of low-frequency fluctuation and gray matter volume loadings in major depressive disorder. The gray matter component of the identified network was negatively related to errors made in Intra-Extra Dimensional Set Shift while positively related to stages completed. These findings may help to deepen our understanding of the pathophysiological mechanisms of cognitive dysfunction in adolescent depression.

Multimodal fusion of multiple rest fMRI networks and MRI gray matter via parallel multilink joint ICA reveals highly significant function/structure coupling in Alzheimer's disease.

In this article, we focus on estimating the joint relationship between structural magnetic resonance imaging (sMRI) gray matter (GM), and multiple functional MRI (fMRI) intrinsic connectivity networks (ICNs). To achieve this, we propose a multilink joint independent component analysis (ml-jICA) method using the same core algorithm as jICA. To relax the jICA assumption, we propose another extension called parallel multilink jICA (pml-jICA) that allows for a more balanced weight distribution over ml-jICA/jICA. We assume a shared mixing matrix for both the sMRI and fMRI modalities, while allowing for different mixing matrices linking the sMRI data to the different ICNs. We introduce the model and then apply this approach to study the differences in resting fMRI and sMRI data from patients with Alzheimer's disease (AD) versus controls. The results of the pml-jICA yield significant differences with large effect sizes that include regions in overlapping portions of default mode network, and also hippocampus and thalamus. Importantly, we identify two joint components with partially overlapping regions which show opposite effects for AD versus controls, but were able to be separated due to being linked to distinct functional and structural patterns. This highlights the unique strength of our approach and multimodal fusion approaches generally in revealing potentially biomarkers of brain disorders that would likely be missed by a unimodal approach. These results represent the first work linking multiple fMRI ICNs to GM components within a multimodal data fusion model and challenges the typical view that brain structure is more sensitive to AD than fMRI.

Neurodegenerative disease after hematopoietic stem cell transplantation in metachromatic leukodystrophy.

Metachromatic leukodystrophy is a lysosomal storage disease caused by deficient arylsulfatase A. It is characterized by progressive demyelination and thus mainly affects the white matter. Hematopoietic stem cell transplantation may stabilize and improve white matter damage, yet some patients deteriorate despite successfully treated leukodystrophy. We hypothesized that post-treatment decline in metachromatic leukodystrophy might be caused by gray matter pathology. Three metachromatic leukodystrophy patients treated with hematopoietic stem cell transplantation with a progressive clinical course despite stable white matter pathology were clinically and radiologically analyzed. Longitudinal volumetric MRI was used to quantify atrophy. We also examined histopathology in three other patients deceased after treatment and compared them with six untreated patients. The three clinically progressive patients developed cognitive and motor deterioration after transplantation, despite stable mild white matter abnormalities on MRI. Volumetric MRI identified cerebral and thalamus atrophy in these patients, and cerebellar atrophy in two. Histopathology showed that in brain tissue of transplanted patients, arylsulfatase A expressing macrophages were clearly present in the white matter, but absent in the cortex. Arylsulfatase A expression within patient thalamic neurons was lower than in controls, the same was found in transplanted patients. Neurological deterioration may occur after hematopoietic stem cell transplantation in metachromatic leukodystrophy despite successfully treated leukodystrophy. MRI shows gray matter atrophy, and histological data demonstrate absence of donor cells in gray matter structures. These findings point to a clinically relevant gray matter component of metachromatic leukodystrophy, which does not seem sufficiently affected by transplantation.

Black Line Sign in Focal Cortical Dysplasia IIB: A 7T MRI and Electroclinicopathologic Study.

We aim to provide detailed imaging-electroclinicopathologic characterization of the black line sign, a novel MRI marker for focal cortical dysplasia (FCD) IIB. 7T T2*-weighted gradient-echo (T2*w-GRE) images were retrospectively reviewed in a consecutive cohort of patients with medically intractable epilepsy with pathology-proven FCD II, for the occurrence of the black line sign. We examined the overlap between the black line region and the seizure-onset zone (SOZ) defined by intracranial EEG (ICEEG) and additionally assessed whether complete inclusion of the black line region in the surgical resection was associated with postoperative seizure freedom. The histopathologic specimen was aligned with the MRI to investigate the pathologic underpinning of the black line sign. Region-of-interest-based quantitative MRI (qMRI) analysis on the 7T T1 map was performed in the black line region, entire lesional gray matter (GM), and contralateral/ipsilateral normal gray and white matter (WM). We included 20 patients with FCD II (14 IIB and 6 IIA). The black line sign was identified in 12/14 (85.7%) of FCD IIB and 0/6 of FCD IIA on 7T T2*w-GRE. The black line region was highly concordant with the ICEEG-defined SOZ (5/7 complete and 2/7 partial overlap). Seizure freedom was seen in 8/8 patients whose black line region was completely included in the surgical resection; in the 2 patients whose resection did not completely include the black line region, both had recurring seizures. Inclusion of the black line region in the surgical resection was significantly associated with seizure freedom (p = 0.02). QMRI analyses showed that the T1 mean value of the black line region was significantly different from the WM (p < 0.001), but similar to the GM. Well-matched histopathologic slices in one case revealed accumulated dysmorphic neurons and balloon cells in the black line region. The black line sign may serve as a noninvasive marker for FCD IIB. Both MRI-pathology and qMRI analyses suggest that the black line region was an abnormal GM component within the FCD. Being highly concordant with ICEEG-defined SOZ and significantly associated with seizure freedom when included in resection, the black line sign may contribute to the planning of ICEEG/surgery of patients with medically intractable epilepsy with FCD IIB. This study provides Class II evidence that in individuals with intractable focal epilepsy undergoing resection who have a 7T MRI with adequate image quality, the presence of the black line sign may suggest FCD IIB, be concordant with SOZ from ICEEG, and be associated with more seizure freedom if fully included in resection.

Neuroanatomical signatures of genetic risk for Alzheimer's disease in healthy adults

AbstractBackgroundEarly detection of Alzheimer’s disease (AD) is critical for the development of interventions that delay or prevent neurodegeneration. Previous studies suggest that genetic liability to AD may be associated with structural brain changes that start 20‐30 years before symptoms manifest.MethodWe derived an optimised AD polygenic risk score (AD‐PRS) using imputed genotype data from 488,377 participants in UK Biobank. We examined the structural brain correlates of AD‐PRS in 27,496 healthy UK Biobank participants using voxel‐based morphometry (VBM) with multiple regression and additionally performed regression analyses using image‐derived phenotypes (IDPs) from T1‐weighted and diffusion‐weighted magnetic resonance images.ResultVBM revealed that high AD‐PRS was associated with reduced grey matter volume (GMV) in bilateral hippocampus and putamen (family wise error correction, p&lt;0.05). IDP analysis further revealed GMV deficits in a bihemispheric limbic network incorporating hippocampus, parahippocampal gyrus, ventral striatum, orbitofrontal cortex, and thalamus, as well as reduced white matter integrity in the major white matter tracts of this network (fornix, cingulum hippocampus, and thalamic radiation).ConclusionGenetic liability to AD is associated with preclinical neurodegeneration in grey and white matter components of the limbic system. Integrity of this network could serve as a marker of preclinical AD and an outcome measure in prevention trials.

Ultra-long-TE arterial spin labeling reveals rapid and brain-wide blood-to-CSF water transport in humans

The study of brain clearance mechanisms is an active area of research. While we know that the cerebrospinal fluid (CSF) plays a central role in one of the main existing clearance pathways, the exact processes for the secretion of CSF and the removal of waste products from tissue are under debate. CSF is thought to be created by the exchange of water and ions from the blood, which is believed to mainly occur in the choroid plexus. This exchange has not been thoroughly studied in vivo.We propose a modified arterial spin labeling (ASL) MRI sequence and image analysis to track blood water as it is transported to the CSF, and to characterize its exchange from blood to CSF. We acquired six pseudo-continuous ASL sequences with varying labeling duration (LD) and post-labeling delay (PLD) and a segmented 3D-GRASE readout with a long echo train (8 echo times (TE)) which allowed separation of the very long-T2 CSF signal. ASL signal was observed at long TEs (793 ms and higher), indicating presence of labeled water transported from blood to CSF. This signal appeared both in the CSF proximal to the choroid plexus and in the subarachnoid space surrounding the cortex. ASL signal was separated into its blood, gray matter and CSF components by fitting a triexponential function with T2s taken from literature. A two-compartment dynamic model was introduced to describe the exchange of water through time and TE. From this, a water exchange time from the blood to the CSF (Tbl->CSF) was mapped, with an order of magnitude of approximately 60 s.

Evaluating the Neuroimaging-Genetic Prediction of Symptom Changes in Individuals with ADHD.

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that could persist into adulthood with known abnormalities in brain structure. Genetics also play an important role in the etiology of the disorder and could affect the disorder trajectory. In this study, we investigated the prediction power of brain image and genomic features for symptom change in 77 individuals with ADHD as part of NeuroIMAGE cohort. Gray matter components and working memory assessments at baseline, as well as gene scores of interest, were used to predict the changes in the two symptom domains: inattentive and hyperactive/impulsive, an average of 4 years. A linear regression model coupled with various feature selection approaches, including leave-one-out-cross-validation (LOOCV), stability selection with resampling, and permutation tests, was implemented to mitigate the overtraining potential caused by small sample sizes. Results showed that traditional LOOCV overestimated the prediction power. We proposed a novel stability selection with the threshold set by permutation tests, which provided more objective assessment. Using our proposed procedure, we identified a statistical promising prediction model for inattention symptom change; the consistent correlation between predicted values and measured values during model training, validating and hold out testing (r=0.64, 0.53, 0.46, respectively), but the p value is not significant in the holdout test. The selected features include age, gray matter in the insula, genes OSBPL1A, CTNNB1, PRPSAP2, ACADM, and polygenic risk score of education attainment, which have been previously reported to be associated with ADHD. We speculate that significant associations may be observed with a large sample size.

Gray Matter Matters: A Longitudinal Magnetic Resonance Voxel-Based Morphometry Study of Primary Progressive Multiple Sclerosis.

Background: Multiple Sclerosis (MS) lesions in white matter (WM) are easily detected with conventional MRI which induce inflammation thereby generating contrast. WM lesions do not consistently explain the extent of clinical disability, cognitive impairment, or the source of an exacerbation. Gray matter (GM) structures including the cerebral cortex and various deep nuclei are known to be affected early in Primary Progressive Multiple Sclerosis (PPMS) and drive disease progression, disability, fatigue, and cognitive dysfunction. However, little is known about how rapidly GM lesions develop and accumulate over time.Objective: The purpose of this study is to analyze the degree and rate of progression in 25 patients with PPMS using voxel-based automated volumetric quantitation.Methods: This is a retrospective single-center study which includes a cohort of 25 patients with PPMS scanned utilizing NeuroQuant® 3 dimensional voxel-based morphometry (3D VBM) automated analysis and database and restudied after a period of ~1 year (11–14 months). Comparisons with normative data were acquired for whole brain, forebrain parenchyma, cortical GM, hippocampus, thalamus, superior and inferior lateral ventricles. GM volume changes were correlated with their clinical motor and cognitive scores using Extended Disability Status Scales (EDSS) and Montreal Cognitive Assessments (MoCA).Results: Steep reductions occurred in cerebral cortical GM and deep GM nuclei volumes which correlated with each patient's clinical and cognitive impairment. The median observed percentile volume losses were statistically significant compared with the 50th percentile for each GM component. Longitudinal assessments of an unselected sample of one dozen patients involved in the PPMS study showed prominent losses occurring mainly in cortical GM and hippocampus which were reflected in their EDSS and MoCA. The longitudinal results were compared with a similar sample of patients having Relapsing MS (RMS) whose GM values were largely in normal range, annualized volume GM changes were much less, while WM hyperintensities were in abnormal range in half the unselected cases.Conclusions: Knowledge of the degree and rapidity with which cortical atrophy and deep GM volume loss develops clarifies the source of progressive cognitive and clinical decline in PPMS.

Machine Learning Classification Identifies Cerebellar Contributions to Early and Moderate Cognitive Decline in Alzheimer's Disease.

Alzheimer’s disease (AD) is one of the most common forms of dementia, marked by progressively degrading cognitive function. Although cerebellar changes occur throughout AD progression, its involvement and predictive contribution in its earliest stages, as well as gray or white matter components involved, remains unclear. We used MRI machine learning-based classification to assess the contribution of two tissue components [volume fraction myelin (VFM), and gray matter (GM) volume] within the whole brain, the neocortex, the whole cerebellum as well as its anterior and posterior parts and their predictive contribution to the first two stages of AD and typically aging controls. While classification accuracy increased with AD stages, VFM was the best predictor for all early stages of dementia when compared with typically aging controls. However, we document overall higher cerebellar prediction accuracy when compared to the whole brain with distinct structural signatures of higher anterior cerebellar contribution to mild cognitive impairment (MCI) and higher posterior cerebellar contribution to mild/moderate stages of AD for each tissue property. Based on these different cerebellar profiles and their unique contribution to early disease stages, we propose a refined model of cerebellar contribution to early AD development.

MRI correlates of clinical disability and hand-motor performance in multiple sclerosis phenotypes

Background: Hand-motor impairment affects a large proportion of multiple sclerosis (MS) patients; however, its substrates are still poorly understood. Objectives: To investigate the association between global disability, hand-motor impairment, and alterations in motor-relevant structural and functional magnetic resonance imaging (MRI) networks in MS patients with different clinical phenotypes. Methods: One hundred thirty-four healthy controls (HC) and 364 MS patients (250 relapsing-remitting MS (RRMS) and 114 progressive MS (PMS)) underwent Expanded Disability Status Scale (EDSS) rating, nine-hole peg test (9HPT), and electronic finger tapping rate (EFTR). Structural and resting state (RS) functional MRI scans were used to perform a source-based morphometry on gray matter (GM) components, to analyze white matter (WM) tract diffusivity indices and to perform a RS seed-based approach from the primary motor cortex involved in hand movement (hand-motor cortex). Random forest analyses identified the predictors of clinical impairment. Result: In RRMS, global measures of atrophy and lesions together with measures of structural damage of motor-related regions predicted EDSS (out-of-bag (OOB)-R2 = 0.19, p-range = <0.001–0.04), z9HPT (right: OOB-R2 = 0.14; left: OOB-R2 = 0.24, p-range = <0.001–0.03). No RS functional connectivity (FC) abnormalities were identified in RRMS models. In PMS, cerebellar and sensorimotor regions atrophy, cerebellar peduncles integrity and increased RS FC between left hand-motor cortex and right inferior frontal gyrus predicted EDSS (OBB-R2 = 0.16, p-range = 0.02–0.04). Conclusion: In RRMS, only measures of structural damage contribute to explain motor impairment, whereas both structural and functional MRI measures predict clinical disability in PMS. A multiparametric MRI approach could be relevant to investigate hand-motor impairment in different MS phenotypes.

White Matter Hyperintensities Contribute to Language Deficits in Primary Progressive Aphasia.

To determine the contribution of white matter hyperintensities (WMH) to language deficits while accounting for cortical atrophy in individuals with primary progressive aphasia (PPA). Forty-three individuals with PPA completed neuropsychological assessments of nonverbal semantics, naming, and sentence repetition plus T2-weighted and fluid-attenuated inversion recovery scans. Using three visual scales, we rated WMH and cerebral ventricle size for both scan types. We used Spearman correlations to evaluate associations between the scales and scans. To test whether visual ratings-particularly of WMH-are associated with language, we compared a base model (including gray matter component scores obtained via principal component analysis, age, and days between assessment and MRI as independent variables) with full models (ie, the base model plus visual ratings) for each language variable. Visual ratings were significantly associated within and between scans and were significantly correlated with age but not with other vascular risk factors. Only the T2 scan ratings were associated with language abilities. Specifically, controlling for other variables, poorer naming was significantly related to larger ventricles (P = 0.033) and greater global (P = 0.033) and periventricular (P = 0.049) WMH. High global WMH (P = 0.034) were also correlated with worse sentence repetition skills. Visual ratings of global brain health were associated with language deficits in PPA independent of cortical atrophy and age. While WMH are not unique to PPA, measuring WMH in conjunction with cortical atrophy may elucidate more accurate brain structure-behavior relationships in PPA than cortical atrophy measures alone.

Non-negative data-driven mapping of structural connections with application to the neonatal brain

Mapping connections in the neonatal brain can provide insight into the crucial early stages of neurodevelopment that shape brain organisation and lay the foundations for cognition and behaviour. Diffusion MRI and tractography provide unique opportunities for such explorations, through estimation of white matter bundles and brain connectivity. Atlas-based tractography protocols, i.e. a priori defined sets of masks and logical operations in a template space, have been commonly used in the adult brain to drive such explorations. However, rapid growth and maturation of the brain during early development make it challenging to ensure correspondence and validity of such atlas-based tractography approaches in the developing brain. An alternative can be provided by data-driven methods, which do not depend on predefined regions of interest. Here, we develop a novel data-driven framework to extract white matter bundles and their associated grey matter networks from neonatal tractography data, based on non-negative matrix factorisation that is inherently suited to the non-negative nature of structural connectivity data. We also develop a non-negative dual regression framework to map group-level components to individual subjects. Using in-silico simulations, we evaluate the accuracy of our approach in extracting connectivity components and compare with an alternative data-driven method, independent component analysis. We apply non-negative matrix factorisation to whole-brain connectivity obtained from publicly available datasets from the Developing Human Connectome Project, yielding grey matter components and their corresponding white matter bundles. We assess the validity and interpretability of these components against traditional tractography results and grey matter networks obtained from resting-state fMRI in the same subjects. We subsequently use them to generate a parcellation of the neonatal cortex using data from 323 new-born babies and we assess the robustness and reproducibility of this connectivity-driven parcellation.

Blood–brain barrier permeability measurement by biexponentially modeling whole‐brain arterial spin labeling data with multiple T2‐weightings

Blood-brain barrier (BBB) permeability assessment remains of ongoing interest in clinical practice and research. Transitions between intravascular (IV) and extravascular (EV) gray matter (GM) compartments may provide information regarding the microstructural status of the BBB. Due to different transverse relaxation times (T2 ) of water protons in vessels and GM, it is possible to determine the compartment in which these protons are located. This work presents and investigates the feasibility of a simplified analytical approach for compartmentalizing the proportions of magnetically marked water protons into IV and EV GM components by biexponentially modeling T2 -weighted arterial spin labeling (ASL) data. Numerous model assumptions were used to stabilize the fit and achieve in vivo applicability. Particularly, transverse relaxation times of IV and EV water protons were determined from the analysis of two supporting T2 -weighted ASL measurements, utilizing a monoexponential signal model. This stabilized a two-parameter biexponential fit of ASL data with T2 preparation (PLD = 0.9/1.2/1.5/1.8 s, TET2Prep = 0/30/40/60/80/120/160 ms), which thereby robustly provided estimates of the IV and EV compartment fractions. Experiments were conducted with three healthy volunteers in a 3 T scanner. Averaged over all subjects, the labeled water protons inherit T2,IV = 200 ± 18 ms initially and adapt T2,EV = 91 ± 2 ms with a longer retention time in cerebral structures. Accordingly, the EVlocated ASL signal fraction rises with increasing PLD from 0.31 ± 0.11 at the shortest PLD of 0.9 s to 0.73 ± 0.02 at the longest PLD of 1.8s. These results indicate a transition of the water protons from IV to EV space. The findings support the potential of biexponential modeling for compartmentalizing ASL spin fractions between IV and EV space. The novel integration of monoexponential parameter estimates stabilizes the two-compartment model fit, suggesting that this technique is suitable for robustly estimating the BBB permeability in vivo.

Holographic Reconstruction of Axonal Pathways in the Human Brain

Three-dimensional documentation of the axonal pathways connecting gray matter components of the human brain has wide-ranging scientific and clinical applications. Recent attempts to map human structural connectomes have concentrated on using tractography results derived from diffusion-weighted imaging data, but tractography is an indirect method with numerous limitations. Advances in holographic visualization platforms provide a new medium to integrate anatomical data, as well as a novel working environment for collaborative interaction between neuroanatomists and brain-imaging scientists. Therefore, we developed the first holographic interface for building axonal pathways, populated it with human histological and structural MRI data, and assembled world expert neuroanatomists to interactively define axonal trajectories of the cortical, basal ganglia, and cerebellar systems. This blending of advanced visualization hardware, software development, and neuroanatomy data enabled the translation of decades of amassed knowledge into a human axonal pathway atlas that can be applied to educational, scientific, or clinical investigations.

Structural and functional brain correlates of theory of mind impairment post-stroke

The ability to understand the mental states of others – also known as Theory of Mind (ToM) – is critical for normal social interactions. We combine behavioural probes with structural and functional brain imaging to provide the first comprehensive analysis of ToM deficits following stroke using the Reading the Mind in the Eyes Test (RMET). First, fMRI was used to identify the functional brain network involved in a non-clinical cohort. Results indicated that, relative to a control task, the RMET increased activity in a widespread functional bilateral network comprising frontal and temporo-parietal areas. To investigate how damage to grey and white matter components of this network can lead to ToM impairment, parcel-based lesion-symptom mapping (PLSM), white-matter tract-wise statistical analysis (TSA) and disconnectome symptom mapping (DSM) were performed using structural images from 64 stroke patients. PLSM results revealed that low scores on the RMET were associated with damage centered around the right posterior frontal gyrus and insula. TSA and DSM results further revealed that low RMET scores were associated with damage to white-matter tracts connecting frontal and temporo-parietal components of the RMET functional network. Together, these findings suggest that making judgements about the mental states of others imposes demands on a large functional network that can easily be disrupted, both by damage to grey matter areas that form part of the network directly, or the white-matter pathways that connect them.

Brainstem pathology in amyotrophic lateral sclerosis and primary lateral sclerosis: A longitudinal neuroimaging study.

Brainstem pathology is a hallmark feature of ALS, yet most imaging studies focus on cortical grey matter alterations and internal capsule white matter pathology. Brainstem imaging in ALS provides a unique opportunity to appraise descending motor tract degeneration and bulbar lower motor neuron involvement. A prospective longitudinal imaging study has been undertaken with 100 patients with ALS, 33 patients with PLS, 30 patients with FTD and 100 healthy controls. Volumetric, vertex and morphometric analyses were conducted correcting for demographic factors to characterise disease-specific patterns of brainstem pathology. Using a Bayesian segmentation algorithm, the brainstem was segmented into the medulla, pons and mesencephalon to measure regional volume reductions, shape analyses were performed to ascertain the atrophy profile of each study group and region-of-interest morphometry was used to evaluate focal density alterations. ALS and PLS patients exhibit considerable brainstem atrophy compared to both disease- and healthy controls. Volume reductions in ALS and PLS are dominated by medulla oblongata pathology, but pontine atrophy can also be detected. In ALS, vertex analyses confirm the flattening of the medullary pyramids bilaterally in comparison to healthy controls and widespread pontine shape deformations in contrast to PLS. The ALS cohort exhibit bilateral density reductions in the mesencephalic crura in contrast to healthy controls, central pontine atrophy compared to disease controls, peri-aqueduct mesencephalic and posterior pontine changes in comparison to PLS patients. CONCLUS: ions: Computational brainstem imaging captures the degeneration of both white and grey matter components in ALS. Our longitudinal data indicate progressive brainstem atrophy over time, underlining the biomarker potential of quantitative brainstem measures in ALS. At a time when a multitude of clinical trials are underway worldwide, there is an unprecedented need for accurate biomarkers to monitor disease progression and detect response to therapy. Brainstem imaging is a promising addition to candidate biomarkers of ALS and PLS.

Regional structural hypo- and hyperconnectivity of frontal-striatal and frontal-thalamic pathways in behavioral variant frontotemporal dementia.

Behavioral variant frontotemporal dementia (bvFTD) has been predominantly considered as a frontotemporal cortical disease, with limited direct investigation of frontal-subcortical connections. We aim to characterize the grey and white matter components of frontal-thalamic and frontal-striatal circuits in bvFTD. Twenty-four patients with bvFTD and 24 healthy controls underwent morphological and diffusion imaging. Subcortical structures were manually segmented according to published protocols. Probabilistic pathways were reconstructed separately from the dorsolateral, orbitofrontal and medial prefrontal cortex to the striatum and thalamus. Patients with bvFTD had smaller cortical and subcortical volumes, lower fractional anisotropy, and higher mean diffusivity metrics, which is consistent with disruptions in frontal-striatal-thalamic pathways. Unexpectedly, regional volumes of the striatum and thalamus connected to the medial prefrontal cortex were significantly larger in bvFTD (by 135% in the striatum, p = .032, and 217% in the thalamus, p = .004), despite smaller dorsolateral prefrontal cortex connected regional volumes (by 67% in the striatum, p = .002, and 65% in the thalamus, p = .020), and inconsistent changes in orbitofrontal cortex connected regions. These unanticipated findings may represent compensatory or maladaptive remodeling in bvFTD networks. Comparisons are made to other neuropsychiatric disorders suggesting a common mechanism of changes in frontal-subcortical networks; however, longitudinal studies are necessary to test this hypothesis.

Double Diffeomorphism: Combining Morphometry and Structural Connectivity Analysis.

The brain is composed of several neural circuits which may be seen as anatomical complexes composed of grey matter structures interconnected by white matter tracts. Grey and white matter components may be modeled as 3-D surfaces and curves, respectively. Neurodevelopmental disorders involve morphological and organizational alterations which cannot be jointly captured by usual shape analysis techniques based on single diffeomorphisms. We propose a new deformation scheme, called double diffeomorphism, which is a combination of two diffeomorphisms. The first one captures changes in structural connectivity, whereas the second one recovers the global morphological variations of both grey and white matter structures. This deformation model is integrated into a Bayesian framework for atlas construction. We evaluate it on a data-set of 3-D structures representing the neural circuits of patients with Gilles de la Tourette syndrome (GTS). We show that this approach makes it possible to localise, quantify, and easily visualise the pathological anomalies altering the morphology and organization of the neural circuits. Furthermore, results also indicate that the proposed deformation model better discriminates between controls and GTS patients than a single diffeomorphism.

Genetics Modulate Gray Matter Variation Beyond Disease Burden in Prodromal Huntington's Disease.

Huntington’s disease (HD) is a neurodegenerative disorder caused by an expansion mutation of the cytosine–adenine–guanine (CAG) trinucleotide in the HTT gene. Decline in cognitive and motor functioning during the prodromal phase has been reported, and understanding genetic influences on prodromal disease progression beyond CAG will benefit intervention therapies. From a prodromal HD cohort (N = 715), we extracted gray matter (GM) components through independent component analysis and tested them for associations with cognitive and motor functioning that cannot be accounted for by CAG-induced disease burden (cumulative effects of CAG expansion and age). Furthermore, we examined genetic associations (at the genomic, HD pathway, and candidate region levels) with the GM components that were related to functional decline. After accounting for disease burden, GM in a component containing cuneus, lingual, and middle occipital regions was positively associated with attention and working memory performance, and the effect size was about a tenth of that of disease burden. Prodromal participants with at least one dystonia sign also had significantly lower GM volume in a bilateral inferior parietal component than participants without dystonia, after controlling for the disease burden. Two single-nucleotide polymorphisms (SNPs: rs71358386 in NCOR1 and rs71358386 in ADORA2B) in the HD pathway were significantly associated with GM volume in the cuneus component, with minor alleles being linked to reduced GM volume. Additionally, homozygous minor allele carriers of SNPs in a candidate region of ch15q13.3 had significantly higher GM volume in the inferior parietal component, and one minor allele copy was associated with a total motor score decrease of 0.14 U. Our findings depict an early genetical GM reduction in prodromal HD that occurs irrespective of disease burden and affects regions important for cognitive and motor functioning.

Co-altered functional networks and brain structure in unmedicated patients with bipolar and major depressive disorders.

Bipolar disorder (BD) and major depressive disorder (MDD) share similar clinical characteristics that often obscure the diagnostic distinctions between their depressive conditions. Both functional and structural brain abnormalities have been reported in these two disorders. However, the direct link between altered functioning and structure in these two diseases is unknown. To elucidate this relationship, we conducted a multimodal fusion analysis on the functional network connectivity (FNC) and gray matter density from MRI data from 13 BD, 40 MDD, and 33 matched healthy controls (HC). A data-driven fusion method called mCCA+jICA was used to identify the co-altered FNC and gray matter components. Comparing to HC, BD exhibited reduced gray matter density in the parietal and occipital cortices, which correlated with attenuated functional connectivity within sensory and motor networks, as well as hyper-connectivity in regions that are putatively engaged in cognitive control. In addition, lower gray matter density was found in MDD in the amygdala and cerebellum. High accuracy in discriminating across groups was also achieved by trained classification models, implying that features extracted from the fusion analysis hold the potential to ultimately serve as diagnostic biomarkers for mood disorders.