Voxel-wise Gray Matter Volume Research Articles

Disentangling gray matter atrophy and its neurotransmitter architecture in drug-naïve Parkinson's disease: an atlas-based correlation analysis.

Parkinson's disease is characterized by multiple neurotransmitter systems beyond the traditional dopaminergic pathway, yet their influence on volumetric alterations is not well comprehended. We included 72 de novo, drug-naïve Parkinson's disease patients and 61 healthy controls. Voxel-wise gray matter volume was evaluated between Parkinson's disease and healthy controls, as well as among Parkinson's disease subgroups categorized by clinical manifestations. The Juspace toolbox was utilized to explore the spatial relationship between gray matter atrophy and neurotransmitter distribution. Parkinson's disease patients exhibited widespread GM atrophy in the cerebral and cerebellar regions, with spatial correlations with various neurotransmitter receptors (FDR-P < 0.05). Cognitively impaired Parkinson's disease patients showed gray matter atrophy in the left middle temporal atrophy, which is associated with serotoninergic, dopaminergic, cholinergic, and glutamatergic receptors (FDR-P < 0.05). Postural and gait disorder patients showed atrophy in the right precuneus, which is correlated with serotoninergic, dopaminergic, gamma-aminobutyric acid, and opioid receptors (FDR-P < 0.05). Patients with anxiety showed atrophy in the right superior orbital frontal region; those with depression showed atrophy in the left lingual and right inferior occipital regions. Both conditions were linked to serotoninergic and dopaminergic receptors (FDR-P < 0.05). Parkinson's disease patients exhibited regional gray matter atrophy with a significant distribution of specific neurotransmitters, which might provide insights into the underlying pathophysiology of clinical manifestations and develop targeted intervention strategies.

Combining Quantitative Susceptibility Mapping With the Gray Matter Volume to Predict Neurological Deficits in Patients With Small Artery Occlusion.

Currently, there is still a lack of valuable neuroimaging markers to assess the clinical severity of stroke patients with small artery occlusion (SAO). Quantitative susceptibility mapping (QSM) is a quantitative processing method for neuroradiological diagnostics. Gray matter (GM) volume changes in stroke patients are also proved to be associated with neurological deficits. This study aims to explore the predictive value of QSM and GM volume in neurological deficits of patients with SAO. As neurological deficits, the National Institutes of Health Stroke Scale (NIHSS) was used. Sixty-six SAO participants within 24h of first onset were enrolled and divided into mild and moderate groups based on NIHSS. QSM values of infarct area and GM volume were calculated from magnetic resonance imaging (MRI) data. Two-sample t-tests were used to compare differences in QSM value and GM volume between the two groups, and the diagnostic efficacy of the combination of QSM value and GM volume was evaluated. The results revealed both the QSM value and GM volume within the infarct area of the moderate group were lower compared to the mild group. Moderate group exhibited lower GM volume in some specific gyrus compared with mild group in the case of voxel-wise GM volume on whole-brain voxel level. The support vector machine (SVM) classifier's analysis showed a high power for the combination of QSM value, GM volume within the infarct area, and voxel-wise GM volume. Our research first reported the combination of QSM value, GM volume within the infarct area, and voxel-wise GM volume could be used to predict neurological impairment of patients with SAO, which provides new insights for further understanding the SAO stroke.

Pseudo-time Series Structural MRI Revealing Progressive Gray Matter Changes with Elevated Intraocular Pressure in Primary Open-Angle Glaucoma: A Preliminary Study

Rationale and ObjectivesPrimary open-angle glaucoma (POAG) is accompanied with gray matter changes across the brain. However, causal relationships of the gray matter changes have not been fully understood. Our aim was to investigate the causality of gray matter progressive changes in POAG using Granger causality (GC) analysis and structural MRI. Materials and MethodsStructural MRI from 20 healthy controls and 30 POAG patients with elevated intraocular pressure (IOP) were collected. We performed voxel-wise gray matter volume comparisons between control and POAG groups, and between control and 4 POAG subgroups (categorized by IOP). Then, we sequenced the structural MRI data of all POAG patients and conducted both voxel-wise and region of interest (ROI)-wise GC analysis to investigate the causality of gray matter volume changes in POAG brain. ResultsCompared with healthy controls, reduced gray matter volumes across the brain were found, gray matter volume enlargements in the thalamus, caudate nucleus and cuneus were also observed in POAG brain (false discovery rate (FDR) corrected at q<0.05). As IOP elevated, the reductions of gray matter volume were more severe in the cerebellum and frontal lobe. GC analysis revealed that the bilateral cerebellum, visual cortices, and the frontal regions served independently as primary hubs of the directional causal network, and projected causal effects to the parietal and temporal regions of the brain (FDR corrected at q<0.05). ConclusionPOAG exhibits progressive gray matter alterations across the brain, with oculomotor regions and visual cortices as independent primary hubs. The current results may deepen our understanding of neuropathology of POAG.

Normal cohorts in automated brain atrophy estimation: how many healthy subjects to include?

ObjectivesThis study investigates the influence of normal cohort (NC) size and the impact of different NCs on automated MRI-based brain atrophy estimation.MethodsA pooled NC of 3945 subjects (NCpool) was retrospectively created from five publicly available cohorts. Voxel-wise gray matter volume atrophy maps were calculated for 48 Alzheimer’s disease (AD) patients (55–82 years) using veganbagel and dynamic normal templates with an increasing number of healthy subjects randomly drawn from NCpool (initially three, and finally 100 subjects). Over 100 repeats of the process, the mean over a voxel-wise standard deviation of gray matter z-scores was established and plotted against the number of subjects in the templates. The knee point of these curves was defined as the minimum number of subjects required for consistent brain atrophy estimation. Atrophy maps were calculated using each NC for AD patients and matched healthy controls (HC). Two readers rated the extent of mesiotemporal atrophy to discriminate AD/HC.ResultsThe maximum knee point was at 15 subjects. For 21 AD/21 HC, a sufficient number of subjects were available in each NC for validation. Readers agreed on the AD diagnosis in all cases (Kappa for the extent of atrophy, 0.98). No differences in diagnoses between NCs were observed (intraclass correlation coefficient, 0.91; Cochran’s Q, p = 0.19).ConclusionAt least 15 subjects should be included in age- and sex-specific normal templates for consistent brain atrophy estimation. In the study’s context, qualitative interpretation of regional atrophy allows reliable AD diagnosis with a high inter-reader agreement, irrespective of the NC used.Clinical relevance statementThe influence of normal cohorts (NCs) on automated brain atrophy estimation, typically comparing individual scans to NCs, remains largely unexplored. Our study establishes the minimum number of NC-subjects needed and demonstrates minimal impact of different NCs on regional atrophy estimation.Key Points• Software-based brain atrophy estimation often relies on normal cohorts for comparisons.• At least 15 subjects must be included in an age- and sex-specific normal cohort.• Using different normal cohorts does not influence regional atrophy estimation.

The Association of Retinal Microvasculature With Gray Matter Changes and Structural Covariance Network: A Voxel-Based Morphometry Study.

Increasing evidence suggests that retinal microvasculature may reflect global cerebral atrophy. However, little is known about the relation of retinal microvasculature with specific brain regions and brain networks. Therefore, we aimed to unravel the association of retinal microvasculature with gray matter changes and structural covariance network using a voxel-based morphometry (VBM) analysis. One hundred and forty-four volunteers without previously known neurological diseases were recruited from West China Hospital, Sichuan University between April 1, 2021, and December 31, 2021. Retinal microvasculature of superficial vascular plexus (SVP), intermediate capillary plexus (ICP), and deep capillary plexus (DCP) were measured by optical coherence tomography angiography using an automatic segmentation. The VBM and structural covariance network analyses were applied to process brain magnetic resonance imaging (MRI) images. The associations of retinal microvasculature with voxel-wise gray matter volumes and structural covariance network were assessed by linear regression models. In the study, 137 participants (mean age = 59.72 years, 37.2% men) were included for the final analysis. Reduced perfusion in SVP was significantly associated with reduced voxel-wise gray matter volumes of the brain regions including the insula, putamen, occipital, frontal, and temporal lobes, all of which were located in the anterior part of the brain supplied by internal carotid artery, except the occipital lobe. In addition, these regions were also involved in visual processing and cognitive impairment (such as left inferior occipital gyrus, left lingual gyrus, and right parahippocampal gyrus). In regard to the structural covariance, the perfusions in SVP were positively related to the structural covariance of the left lingual gyrus seed with the left middle occipital gyrus, the right middle occipital gyrus, and the left middle frontal gyrus. Poor perfusion in SVP was correlated with reduced voxel-wise gray matter volumes and structural covariance networks in regions related to visual processing and cognitive impairment. It suggests that retinal microvasculature may offer a window to identify aging related cerebral alterations.

Divergent brain regional atrophy and associated fiber disruption in amnestic and non-amnestic MCI

BackgroundUnderstanding the pathological characteristics of various mild cognitive impairment (MCI) subtypes is crucial for the differential diagnosis of dementia. The purpose of this study was to feature divergent symptom-deficit profiles in amnestic MCI (aMCI) and non-amnestic MCI (naMCI).MethodsT1 and DTI MRI data from a total of 158 older adults with 50 normal controls, 56 aMCI, and 52 naMCI were included. The voxel-wise gray matter volumes and the number of seed-based white matter fiber bundles were compared among these three groups. Furthermore, correlation and mediation analyses between the neuroimaging indices and cognitive measures were performed.ResultsThe aMCI with specific memory abnormalities was characterized by volumetric atrophy of the left hippocampus but not by damage in the linked white matter fiber bundles. Conversely, naMCI was characterized by both the altered volume of the right inferior frontal gyrus and the significant damage to fiber bundles traversing the region in all three directions, not only affecting fibers around the atrophied area but also distant fibers. Mediation analyses of gray matter-white matter-cognition showed that gray matter atrophy affects the number of fiber bundles and further affects attention and executive function. Meanwhile, fiber bundle damage also affects gray matter volume, which further affects visual processing and language.ConclusionsThe divergent structural damage patterns of the MCI subtypes and cognitive dysfunctions highlight the importance of detailed differential diagnoses in the early stages of pathological neurodegenerative diseases to deepen the understanding of dementia subtypes and inform targeted early clinical interventions.

Neuroanatomical correlates of cognitive impairment following basal ganglia-thalamic post-hemorrhagic stroke: Uncovering network-wide alterations in hemispheric gray matter asymmetry

Cognitive impairment and recovery are central issues in hemorrhagic stroke. This study aimed to investigate whether post-hemorrhagic stroke cognitive impairment (PhSCI) is associated with cortical gray matter (GM) loss and hemispheric asymmetry changes and whether these changes could predict improvements in cognitive function during the recovery. Nineteen patients with PhSCI, comprising 10 with basal ganglia hemorrhage and 9 with thalamic hemorrhage, were recruited. Among them, 9 completed a course of repetitive transcranial magnetic stimulation (rTMS). Additionally, 19 demographically and comorbidity-matched healthy controls were also included. Structural brain MRI and cognitive assessments were performed. Voxel-wise GM volume and hemispheric asymmetry were analyzed. The PhSCI patients exhibited bilateral, yet asymmetric, GM losses in the hippocampus, fusiform, lateral temporal, prefrontal, somatomotor, and inferior parietal regions. The analysis of GM asymmetry revealed that patients showed rightward GM in the lateral temporal, somatomotor, and inferior parietal regions. Among the 9 PhSCI patients who completed rTMS, there was a marginal trend of regional GM increase and leftward GM, and these changes were in parallel with the improvements in cognitive tests. Further lesion connectivity and metanalytic mapping identified two interconnected systems linked to the lesions, which were anchored in the default mode, somatomotor, and salience/cognitive control networks and in the cognitive domains of memory, language, decision-making, and executive function. In conclusion, PhSCI patients exhibited network-wide cortical GM losses, distal to subcortical hemorrhagic lesions, and hemisphere asymmetry changes. These changes appear to predict rTMS-related cognitive improvements, suggesting that even subcortical focal lesions can lead to alterations in distal cortical neuroanatomical architecture. Our preliminary findings provide new insights into the neuroanatomical basis of PhSCI.

Differential Cortical Volume and Surface Morphometry in Youth With Chronic Health Conditions.

Up to 1 in 3 youth in the United States have a childhood-onset chronic health condition (CHC), which can lead to neurodevelopmental disruptions in cognitive functioning and brain structure. However, the nature and extent of structural neurobiomarkers that may be consistent across a broad spectrum of CHCs are unknown. Thus, the purpose of this study was to identify potential differences in brain structure in youth with and without chronic physical health conditions (e.g., diabetes, hemophilia). Here, 49 T1 structural magnetic resonance imaging (MRI) images were obtained from youth with (n = 26) and without (n = 23) CHCs. Images were preprocessed using voxel-based morphometry (VBM) to generate whole-brain voxel-wise gray matter volume maps and whole-brain extracted estimates of cortical surface area and cortical thickness. Multi-scanner harmonization was implemented on surface-based estimates and linear models were used to estimate significant main effects of the group. We detected widespread decreases in brain structure in youth with CHCs as compared to controls in regions of the prefrontal, cingulate, and visual association areas. The insula exhibited the opposite effect, with cases having increased surface area as compared to controls. To our knowledge, these findings identify a novel structural biomarker of childhood-onset CHCs, with consistent alterations identified in gray matter of regions in the prefrontal cortex and insula involved in emotion regulation and executive function. These findings, while exploratory, may reflect an impact of chronic health stress in the adolescent brain, and suggest that more comprehensive assessment of stress and neurodevelopment in youth with CHCs may be appropriate.

Unraveling neurotransmitter changes in de novo GBA-related and idiopathic Parkinson's disease

BackgroundPresently, neurotransmitter deficits in GBA-related Parkinson's disease (GBA-PD) and relationships with cognitive impairment are poorly understood. A better understanding of neurotransmitter impairments in GBA-PD — particularly in the newly diagnosed drug-naïve phase — may support developing targeted intervention strategies. We aimed to investigate patterns of neurotransmitter deficits in GBA-PD and idiopathic PD (iPD) and cognitive performance correlations. MethodsWe recruited 189 newly diagnosed PD patients for GBA sequencing. Voxel-wise gray matter volume (GMV) was evaluated in a subgroup of 17 GBA-PD, 100 iPD, and 32 age- and sex-matched healthy controls (HCs). The JuSpace toolbox covering various neurotransmitter maps helped assess whether the spatial patterns of GMV alterations in GBA-PD or iPD patients (relative to HCs) were associated with specific neurotransmitter systems. ResultsGBA-PD patients indicated widespread GM atrophy in the fronto-temporal-occipital region compared with HCs. GMV atrophy was spatially correlated in GBA-PD and iPD with serotonergic, dopaminergic, and acetylcholinergic pathway distributions (p < 0.05, false discovery rate corrected). Executive function and language in cognitive domains were also associated with the strength of GMV colocalization of serotonergic, dopaminergic, and acetylcholinergic circuits. ConclusionsRegional GM atrophy related to specific neurotransmitter deficits in de novo GBA-PD and iPD patients could provide new insights into pathophysiological processes, facilitating potential therapeutic targets to support PD management.

Serum cortisol is negatively related to hippocampal volume, brain structure, and memory performance in healthy aging and Alzheimer's disease.

Elevated cortisol levels have been frequently reported in Alzheimer's disease (AD) and linked to brain atrophy, especially of the hippocampus. Besides, high cortisol levels have been shown to impair memory performance and increase the risk of developing AD in healthy individuals. We investigated the associations between serum cortisol levels, hippocampal volume, gray matter volume and memory performance in healthy aging and AD. In our cross-sectional study, we analyzed the relationships between morning serum cortisol levels, verbal memory performance, hippocampal volume, and whole-brain voxel-wise gray matter volume in an independent sample of 29 healthy seniors (HS) and 29 patients along the spectrum of biomarker-based AD. Cortisol levels were significantly elevated in patients with AD as compared to HS, and higher cortisol levels were correlated with worse memory performance in AD. Furthermore, higher cortisol levels were significantly associated with smaller left hippocampal volumes in HS and indirectly negatively correlated to memory function through hippocampal volume. Higher cortisol levels were further related to lower gray matter volume in the hippocampus and temporal and parietal areas in the left hemisphere in both groups. The strength of this association was similar in HS and AD. In AD, cortisol levels are elevated and associated with worse memory performance. Furthermore, in healthy seniors, higher cortisol levels show a detrimental relationship with brain regions typically affected by AD. Thus, increased cortisol levels seem to be indirectly linked to worse memory function even in otherwise healthy individuals. Cortisol may therefore not only serve as a biomarker of increased risk for AD, but maybe even more importantly, as an early target for preventive and therapeutic interventions.

Higher brain structural heterogeneity in schizophrenia

As a highly heterogeneous disorder, schizophrenia shows notable interindividual variation in clinical manifestations. On that account, an increasing number of studies begin to examine the interindividual variability in neuroimaging characterization in schizophrenia. However, whether schizophrenia demonstrates higher interindividual morphological variability than health controls (HCs) remains unknown. T1-weighted anatomical images were obtained from patients with schizophrenia (n = 61) and matched HCs (n = 73). For each subject, voxel-wise gray matter volume was obtained using voxel-based morphometry analysis. We first inquired whether patients with schizophrenia showed higher interindividual structural variation than HCs using the person based similarity index (PBSI). Then, we examined differences of voxel-wise morphological coefficient of variation (CV) between schizophrenia and HCs. To further associate identified regions showing higher variability in schizophrenia with cognitive/functional processes, functional annotation was performed. Patients with schizophrenia exhibited lower PBSIs than matched HCs, suggesting higher interindividual morphological variability in schizophrenia. The following results showed that patients with schizophrenia exhibited higher CVs than HCs in distributed brain regions including the striatum, hippocampus, thalamus, parahippocampa gyrus, frontal gyrus, and amygdala. Brain regions showing higher CVs in schizophrenia were significantly implicated in affective, incentive and reward related terms. These results provide a new insight into the high clinical heterogeneity and facilitate personalized diagnose and treatment in schizophrenia.

Reconfigured metabolism brain network in asymptomatic microtubule-associated protein tau mutation carriers: a graph theoretical analysis

BackgroundStudies exploring topological properties of the metabolic network during the presymptomatic stage of genetic frontotemporal dementia (FTD) are scarce. However, such knowledge is important for understanding brain function and disease pathogenesis. Therefore, we aimed to explore FTD-specific patterns of metabolism topology reconfiguration in microtubule-associated protein tau (MAPT) mutation carriers before the onset of symptoms.MethodsSix asymptomatic carriers of the MAPT P301L mutation were compared with 12 non-carriers who all belonged to the same family of FTD. For comparison, we included 32 behavioral variant FTD (bvFTD) patients and 33 unrelated healthy controls. Each participant underwent neuropsychological assessments, genetic testing, and a hybrid positron emission tomography (PET)/magnetic resonance imaging (MRI) scan. Voxel-wise gray matter volumes and standardized uptake value ratios were calculated and compared for structural MRI and fluorodeoxyglucose (FDG)-PET, separately. The sparse inverse covariance estimation method (SICE) was applied to topological properties and metabolic connectomes of brain functional networks derived from 18F-FDG PET/MRI data. Independent component analysis was used to explore the metabolic connectivity of the salience (SN) and default mode networks (DMN).ResultsThe asymptomatic MAPT carriers performed normal global parameters of the metabolism network, whereas bvFTD patients did not. However, we revealed lost hubs in the ventromedial prefrontal, orbitofrontal, and anterior cingulate cortices and reconfigured hubs in the anterior insula, precuneus, and posterior cingulate cortex in asymptomatic carriers compared with non-carriers, which overlapped with the comparisons between bvFTD patients and controls. Similarly, significant differences in local parameters of these nodes were present between asymptomatic carriers and non-carriers. The reduction in the connectivity of lost hub regions and the enhancement of connectivity between reconfigured hubs and components of the frontal cortex were marked during the asymptomatic stage. Metabolic connectivity within the SN and DMN was enhanced in asymptomatic carriers compared with non-mutation carriers but reduced in bvFTD patients relative to controls.ConclusionsOur findings showed that metabolism topology reconfiguration, characterized by the earliest involvement of medial prefrontal areas and active compensation in task-related regions, was present in the presymptomatic phase of genetic FTD with MAPT mutation, which may be used as an imaging biomarker of increased risk of FTD.

Multiomics Analysis of Structural Magnetic Resonance Imaging of the Brain and Cerebrospinal Fluid Metabolomics in Cognitively Normal and Impaired Adults.

IntroductionIntegrating brain imaging with large scale omics data may identify novel mechanisms of mild cognitive impairment (MCI) and early Alzheimer’s disease (AD). We integrated and analyzed brain magnetic resonance imaging (MRI) with cerebrospinal fluid (CSF) metabolomics to elucidate metabolic mechanisms and create a “metabolic map” of the brain in prodromal AD.MethodsIn 145 subjects (85 cognitively normal controls and 60 with MCI), we derived voxel-wise gray matter volume via whole-brain structural MRI and conducted high-resolution untargeted metabolomics on CSF. Using a data-driven approach consisting of partial least squares discriminant analysis, a multiomics network clustering algorithm, and metabolic pathway analysis, we described dysregulated metabolic pathways in CSF mapped to brain regions associated with MCI in our cohort.ResultsThe multiomics network algorithm clustered metabolites with contiguous imaging voxels into seven distinct communities corresponding to the following brain regions: hippocampus/parahippocampal gyrus (three distinct clusters), thalamus, posterior thalamus, parietal cortex, and occipital lobe. Metabolic pathway analysis indicated dysregulated metabolic activity in the urea cycle, and many amino acids (arginine, histidine, lysine, glycine, tryptophan, methionine, valine, glutamate, beta-alanine, and purine) was significantly associated with those regions (P < 0.05).ConclusionBy integrating CSF metabolomics data with structural MRI data, we linked specific AD-susceptible brain regions to disrupted metabolic pathways involving nitrogen excretion and amino acid metabolism critical for cognitive function. Our findings and analytical approach may extend drug and biomarker research toward more multiomics approaches.

Decreased gray matter volume is associated with theory of mind deficit in adolescents with schizophrenia.

Schizophrenia patients often suffer from deficit in theory of mind (TOM). Prior neuroimaging studies revealed neuroimaging correlates of TOM deficit in adults with schizophrenia, neuroimaging correlates of TOM in adolescents is less well established. This study aimed to investigate gray matter volume (GMV) abnormalities and TOM deficits in schizophrenic adolescents, and examine the relationship between them. Twenty adolescent schizophrenic patients and 25 age, sex-matched healthy controls underwent T1-weighted magnetic resonance imaging (MRI) scans, and were examined for TOM based on the Reading the Mind in the Eyes test (RMET). Univariate voxel-based morphometry (VBM) and multivariate source-based morphometry (SBM) were employed to examine alterations of two GMV phenotypes in schizophrenic adolescents: voxel-wise GMV and covarying structural brain patterns (SBPs). Compared with controls, our results revealed a significant deficit in RMET performance of the patients, Voxel-wise VBM analysis revealed that patients exhibited decreased GMV in bilateral insula, orbitofrontal cortex, and right rolandic operculum, and GMV of these brain regions were positively correlated with RMET performance. Multivariate SBM analysis identified a significantly different between-group SBP comprising of bilateral insula and inferior frontal cortex, bilateral superior temporal cortex, and bilateral lateral parietal cortex and right rolandic operculum. The loading scores of this SBP was positively correlated with RMET performance. This study revealed impairment of TOM ability in schizophrenic adolescents and revealed an association between TOM deficit and decreased GMV in regions which are crucial for social cognition, thereby provided insight and possible target regions for understanding the neural pathology and normalizing TOM deficit in adolescent schizophrenia patients.

The correspondence between morphometric MRI and metabolic profile in Rasmussen’s encephalitis

Volumetric magnetic resonance imaging (MRI) atrophy is a hallmark of Rasmussen’s encephalitis (RE). Here, we aim to investigate voxel-wise gray matter (GM) atrophy in RE, and its associations with glucose hypometabolism and neurotransmitter distribution utilizing MRI and PET data. In this study, fifteen RE patients and fourteen MRI normal subjects were included in this study. Voxel-wise GM volume and glucose metabolic uptake were evaluated using structural MRI and FDG-PET images, respectively. Spatial Spearman’s correlation was performed between GM atrophy of RE with FDG uptake alterations, and neurotransmitter distributions provided in the JuSpace toolbox. Compared with the control group, RE patients displayed extensive GM volume loss not only in the ipsilateral hemisphere, but also in the frontal lobe, basal ganglia, and cerebellum in the contralateral hemisphere. Within the RE group, the insular and temporal cortices exhibited significantly more GM atrophy on the ipsilesional than the contralesional side. FDG-PET data revealed significant hypometabolism in areas surrounding the insular cortices in the ipsilesional hemisphere. RE-related GM volumetric atrophy was spatially correlated with hypomebolism in FDG uptake, and with spatial distribution of the dopaminergic and serotonergic neurotransmitter systems. The spatial concordance of morphological changes with metabolic abnormalities suggest FDG-PET offers potential value for RE diagnosis. The GM alterations associated with neurotransmitter distribution map could provide novel insight in understanding the neuropathological mechanisms and clinical feature of RE.

Association of Reproductive History With Brain MRI Biomarkers of Dementia Risk in Midlife.

To examine associations between indicators of estrogen exposure from women's reproductive history and brain MRI biomarkers of Alzheimer disease (AD) in midlife. We evaluated 99 cognitively normal women 52 ± 6 years of age and 29 men 52 ± 7 years of age with reproductive history data, neuropsychological testing, and volumetric MRI scans. We used multiple regressions to examine associations among reproductive history indicators, voxel-wise gray matter volume (GMV), and memory and global cognition scores, adjusting for demographics and midlife health indicators. Exposure variables were menopause status, age at menarche, age at menopause, reproductive span, hysterectomy status, number of children and pregnancies, and use of menopause hormonal therapy (HT) and hormonal contraceptives (HC). All menopausal groups exhibited lower GMV in AD-vulnerable regions compared to men, with perimenopausal and postmenopausal groups also exhibiting lower GMV in temporal cortex compared to the premenopausal group. Reproductive span, number of children and pregnancies, and use of HT and HC were positively associated with GMV, chiefly in temporal cortex, frontal cortex, and precuneus, independent of age, APOE ε4 status, and midlife health indicators. Although reproductive history indicators were not directly associated with cognitive measures, GMV in temporal regions was positively associated with memory and global cognition scores. Reproductive history events signaling more estrogen exposure such as premenopausal status, longer reproductive span, higher number of children, and use of HT and HC were associated with larger GMV in women in midlife. Further studies are needed to elucidate sex-specific biological pathways through which reproductive history influences cognitive aging and AD risk.

Focal white matter microstructural alteration after anthracycline-based systemic treatment in long-term breast cancer survivors: a structural magnetic resonance imaging study.

Understanding the neural correlates ofcognitive problems in patients with breast cancer (BC) after systemic treatment have been a topic of increasinginvestigation. The heterogeneity of the systemic treatment regimens may undermine our ability to identify brain microstructural alterations resulting from any given regimen. We investigated the detrimental effects of the anthracycline-based systemic treatment (AST) regimen (epirubicin and cyclophosphamide + docetaxel + tamoxifen) on brain gray matter (GM) and white matter (WM) microstructural alteration in long-term BC survivors. We performed a battery of neuropsychological tests and structural magnetic resonance imaging (MRI) to 31 long-term BC survivors who had received the AST regimen (AST group) and 43 healthy controls (HC group). Voxel-based morphometry evaluated the whole-brain voxel-wise GM volume, while diffusion tensor imaging technique with tract-based spatial statistics analysis evaluated whole-brain WM microstructural alteration. Partial least squares regression (PLSR) was used to evaluate the relationship between cognitive impairment and brain microstructural alteration in BC survivors. Compared with the HC group, the AST group exhibited a significantly poorer performance in attention, as well as a marginal significantly poorer performance in verbal working memory and executive function. Significantly lower fractional anisotropy (FA), higher radial diffusivity (RD), and lower axial diffusivity (AD) in multiple brain WM regions were showed in AST group compared with the HC group. Overlap of lower FA and higher RD was found in the body of corpus callosum (CC) and bilateral superior corona radiata (SCR), whereas overlap of lower FA and AD was found in the body of CC and right SCR. The PLSR results showed that the WM regions with overlap of lower FA and AD were significantly associated with executive and verbal working memory decline. No significant difference was observed in the GM volume between groups. Our results suggest that microstructural abnormalities of certain vulnerable WM regions in the AST regimen-exposed brain may provide neuroimaging evidence for the identification of brain injury and cognitive impairment induced by specific chemotherapy regimens.

Neurodevelopmental Outcomes following Intrauterine Growth Restriction and Very Preterm Birth

To evaluate whether intrauterine growth restriction (IUGR) adds further neurodevelopmental risk to that posed by very preterm birth alone in terms of alterations in brain growth and poorer toddlerhood outcomes. Participants were 314 infants of very preterm birthenrolled in the Evaluation of Preterm Imaging Study (e-Prime) who were subsequently followed up in toddlerhood. IUGR was identified postnatally from discharge records (n=49) and defined according to prenatal evaluation of growth restriction confirmed by birth weight <10th percentile for gestational age and/or alterations in fetal Doppler. Appropriate for gestational age (AGA; n=265) was defined as birth weight >10th percentile for gestational age at delivery. Infants underwent magnetic resonance imaging at term-equivalent age (median=42weeks); T2-weighted images were obtained for voxelwise gray matter volumes. Follow-up assessments were conducted at corrected median age of 22months using the Bayley Scales of Infant and Toddler Development III and the Modified-Checklist for Autism in Toddlers. Infants of very preterm birth with IUGR displayed a relative volumetric decrease in gray matter in limbic regions and a relative increase in frontoinsular, temporal-parietal, and frontal areas compared with peers of very preterm birth who were AGA. At follow-up, toddlers born very preterm with IUGR had significantly lower cognitive (effect size=0.42) and motor (effect size=0.41) scores and were more likely to have a positive Modified-Checklist for Autism in Toddlers screening for autism (OR=2.12) compared with peers of very preterm birth who were AGA. IUGR might confer a neurodevelopmental risk that is greater than that posed by very preterm alone, in terms of both alterations in brain growth and poorer toddlerhood outcomes.

Predicting intelligence from brain gray matter volume

A positive association between brain size and intelligence is firmly established, but whether region-specific anatomical differences contribute to general intelligence remains an open question. Results from voxel-based morphometry (VBM) - one of the most widely used morphometric methods - have remained inconclusive so far. Here, we applied cross-validated machine learning-based predictive modeling to test whether out-of-sample prediction of individual intelligence scores is possible on the basis of voxel-wise gray matter volume. Features were derived from structural magnetic resonance imaging data (N = 308) using (a) a purely data-driven method (principal component analysis) and (b) a domain knowledge-based approach (atlas parcellation). When using relative gray matter (corrected for total brain size), only the atlas-based approach provided significant prediction, while absolute gray matter (uncorrected) allowed for above-chance prediction with both approaches. Importantly, in all significant predictions, the absolute error was relatively high, i.e., greater than ten IQ points, and in the atlas-based models, the predicted IQ scores varied closely around the sample mean. This renders the practical value even of statistically significant prediction results questionable. Analyses based on the gray matter of functional brain networks yielded significant predictions for the fronto-parietal network and the cerebellum. However, the mean absolute errors were not reduced in contrast to the global models, suggesting that general intelligence may be related more to global than region-specific differences in gray matter volume. More generally, our study highlights the importance of predictive statistical analysis approaches for clarifying the neurobiological bases of intelligence and provides important suggestions for future research using predictive modeling.

Individual prediction of psychotherapy outcome in posttraumatic stress disorder using neuroimaging data

Trauma-focused psychotherapy is the first-line treatment for posttraumatic stress disorder (PTSD) but 30–50% of patients do not benefit sufficiently. We investigated whether structural and resting-state functional magnetic resonance imaging (MRI/rs-fMRI) data could distinguish between treatment responders and non-responders on the group and individual level. Forty-four male veterans with PTSD underwent baseline scanning followed by trauma-focused psychotherapy. Voxel-wise gray matter volumes were extracted from the structural MRI data and resting-state networks (RSNs) were calculated from rs-fMRI data using independent component analysis. Data were used to detect differences between responders and non-responders on the group level using permutation testing, and the single-subject level using Gaussian process classification with cross-validation. A RSN centered on the bilateral superior frontal gyrus differed between responders and non-responder groups (PFWE < 0.05) while a RSN centered on the pre-supplementary motor area distinguished between responders and non-responders on an individual-level with 81.4% accuracy (P < 0.001, 84.8% sensitivity, 78% specificity and AUC of 0.93). No significant single-subject classification or group differences were observed for gray matter volume. This proof-of-concept study demonstrates the feasibility of using rs-fMRI to develop neuroimaging biomarkers for treatment response, which could enable personalized treatment of patients with PTSD.