FreeSurfer V6 Research Articles

Is it importance to take co-occurrence of obesity and cigarette smoking into account in brain imaging studies in major depressive disorder?

Objective: To date, a small number of studies have investigated cortical thickness, cortical surface area, and subcortical volume abnormalities in first-episode, untreated patients with Major Depressive Disorder (MDD). The findings of previous studies are not entirely consistent. Previous studies did not match first-episode, untreated patients with MDD to controls regarding body mass index (BMI) and smoking, which could contribute to the inconsistency of results. The aim of the current study was to examine whether morphological abnormalities are present in first-episode and untreated MDD patients in comparison with well-matched controls, particularly concerning BMI and smoking status. Method: Twenty first-episode, untreated patients with MDD were enrolled in the study along with 20 healthy controls (HC) matched for age, education, sex, BMI and smoking status. Thickness and area of the cortex and subcortical volumes were measured using surface-based morphometry implemented with Freesurfer (v5.3.0). Results: There were no significant differences in cortical thickness, surface area, and subcortical volumes between the first-episode, untreated patients with MDD and HC groups. Conclusion: This study provides evidence that cortical thickness, cortical surface area, and subcortical volumes might be normal in first-episode untreated patients with MDD in comparison with well-matched controls, particularly for BMI and smoking status.

Weight gain as a predictor of frontal and temporal lobe volume loss in bipolar disorder: A prospectiveMRIstudy

A sizable fraction of people with bipolar I disorder (BDI) experience a deteriorating clinical course with increasingly frequent mood episodes and chronic disability. This is believed to result from neurobiological illness progression, or neuroprogression. Excessive weight gain predicts neuroprogression across multiple brain illnesses, but no prospective studies have investigated this in BDI. The objective of this study was to determine whether BDI patients who experienced clinically significant weight gain (CSWG; gaining ≥7% of baseline weight) over 12months had greater 12-month brain volume loss in frontal and temporal regions important to BDI. In 55 early-stage BDI patients we measured (i) rates of CSWG, (ii) the number of days with mood symptoms, using NIMH LifeCharts, and (iii) baseline and 12-month brain volumes, using 3T MRI. We quantified brain volumes using the longitudinal processing stream in FreeSurfer v6.0. We used general linear models for repeated measures to investigate whether CSWG predicted volume loss, adjusting for potentially confounding clinical and treatment variables. After correction for multiple comparisons, CSWG in patients predicted greater volume loss in the left orbitofrontal cortex (effect size [ES; Cohen's d]=-1.01, P=0.002), left cingulate gyrus (ES=-1.31, P<0.001), and left middle temporal gyrus (ES=-0.96, P=0.004). Middle temporal volume loss predicted more days with depression (β=-0.406, P=0.010). These are the first prospective data on weight gain and neuroprogression in BDI. CSWG predicted neuroprogression, and neuroprogression predicted a worse clinical illness course. Trials of weight loss interventions are needed to confirm the causal direction of the weight gain-neuroprogression relationship, and to determine whether weight loss is a disease-modifying treatment.

Genetic architecture of hippocampal subfields on standard resolution MRI: How the parts relate to the whole.

The human hippocampus can be subdivided into subfields with unique functional properties and differential vulnerability to disease or neuropsychiatric conditions. Identifying genes that confer susceptibility to such processes is an important goal in developing treatments. Recent advances in automatic subfield segmentation from magnetic resonance images make it possible to use these measures as phenotypes in large-scale genome-wide association studies. Such analyses are likely to rely largely on standard resolution (~1 mm isotropic) T1 -weighted images acquired on 3.0T scanners. Determining whether the genetic architecture of subfields can be detected from such images is therefore an important step. We used Freesurfer v6.0 to segment hippocampal subfields in two large twin studies, the Vietnam Era Twin Study of Aging and the Human Connectome Project. We estimated heritability of subfields and the genetic overlap with total hippocampal volume. Heritability was similar across samples, but little genetic variance remained after accounting for genetic influences on total hippocampal volume. Importantly, we examined genetic relationships between subfields to determine whether subfields can be grouped based on a smaller number of underlying, genetically independent factors. We identified three genetic factors in both samples, but the high degree of cross loadings precluded formation of genetically distinct groupings of subfields. These results confirm the reliability of Freesurfer v6.0 generated subfields across samples for phenotypic analyses. However, the current results suggest that it will be difficult for large-scale genetic analyses to identify subfield-specific genes that are distinct from both total hippocampal volume and other subfields using segmentations generated from standard resolution T1 -weighted images.

Alzheimer's Disease Biomarkers Have Distinct Associations with Specific Hippocampal Subfield Volumes.

Measures of amyloid-β (Aβ) and phosphorylated tau (p-tau) concentrations in cerebrospinal fluid are extensively used for diagnostic and research purposes in Alzheimer's disease (AD) as correlates of cortical thinning and cognitive outcomes. The present study investigated the relationship of Aβ and p-tau with hippocampal subfield volumes Cornu Ammonis (CA) 1-4, dentate gyrus (DG), and subiculum. Subfields were segmented from T1-weighted images from the ADNI-population using FreeSurfer v6. Linear and polynomial regression models revealed distinct associations of Aβ and p-tau with subfield volumes. Aβ had a quadratic relationship with all hippocampal subfield volumes and the inflection point was higher than the validated cut-off for Aβ. For p-tau the relationships were linear, except for CA3, in which it was quadratic. For the CA1 and CA3, these quadratic relationships with Aβ were only observed when p-tau was low. Amyloid and p-tau contributed equally to the explained variance in CA4 and DG volume. Subicular volume was best explained by Aβ alone. These biomarker relationships with hippocampal subfield volumes seem to mirror the hippocampal-specific topography of Aβ and tau reported in neuropathological staging models. In addition, using continuous values of Aβ reveals positive patterns with imaging markers for individuals around the positivity threshold that would be masked when using dichotomized biomarker groups, which can be important for early detection and accurate inclusion of potential participants at risk for AD in clinical trials.

Hippocampal subfields in adolescent anorexia nervosa

Patients with anorexia nervosa (AN) exhibit volume reduction in cerebral gray matter (GM), and several studies report reduced hippocampus volume. The hippocampal subfields (HS) are functionally and structurally distinct, and appear to respond differently to neuropathology. The aim of this study was to investigate HS volumes in adolescent females with restrictive AN compared to a healthy age-matched control group (HC). The FreeSurfer v6.0 package was used to extract brain volumes, and segment HS in 58 female adolescents (AN = 30, HC = 28). We investigated group differences in GM, white matter (WM), whole hippocampus and 12 HS volumes. AN patients had significantly lower total GM and total hippocampal volume. No group difference was found in WM. Volume reduction was found in 11 of the 12 HS, and most results remained significant when adjusting for global brain volume reduction. Investigations of clinical covariates revealed statistically significant relationships between the whole hippocampus, several HS and scores on depression and anxiety scales in AN. Results from this study show that young AN patients exhibit reduced volume in most subfields of the hippocampus, and that this reduction may be more extensive than the observed global cerebral volume loss.

Brain scans from 21,297 individuals reveal the genetic architecture of hippocampal subfield volumes

The hippocampus is a heterogeneous structure, comprising histologically distinguishable subfields. These subfields are differentially involved in memory consolidation, spatial navigation and pattern separation, complex functions often impaired in individuals with brain disorders characterized by reduced hippocampal volume, including Alzheimer’s disease (AD) and schizophrenia. Given the structural and functional heterogeneity of the hippocampal formation, we sought to characterize the subfields’ genetic architecture. T1-weighted brain scans (n = 21,297, 16 cohorts) were processed with the hippocampal subfields algorithm in FreeSurfer v6.0. We ran a genome-wide association analysis on each subfield, co-varying for whole hippocampal volume. We further calculated the single-nucleotide polymorphism (SNP)-based heritability of 12 subfields, as well as their genetic correlation with each other, with other structural brain features and with AD and schizophrenia. All outcome measures were corrected for age, sex and intracranial volume. We found 15 unique genome-wide significant loci across six subfields, of which eight had not been previously linked to the hippocampus. Top SNPs were mapped to genes associated with neuronal differentiation, locomotor behaviour, schizophrenia and AD. The volumes of all the subfields were estimated to be heritable (h2 from 0.14 to 0.27, all p < 1 × 10–16) and clustered together based on their genetic correlations compared with other structural brain features. There was also evidence of genetic overlap of subicular subfield volumes with schizophrenia. We conclude that hippocampal subfields have partly distinct genetic determinants associated with specific biological processes and traits. Taking into account this specificity may increase our understanding of hippocampal neurobiology and associated pathologies.

A15943 Cerebral atrophy in patients with type 2 diabetes and left ventricular hypertrophy

Objectives: Type 2 diabetes mellitus (T2DM) is associated with an increased risk of dementia. Left ventricular hypertrophy (LVH) is prevalent in T2DM and an independent predictor of cognitive impairment. In this preliminary analysis, we compared brain atrophy between healthy participants and T2DM, and those with T2DM according to the presence or not of LVH. Methods: Non-demented healthy participants without diabetes (n = 37, controls) participating in the Cognition And Neocortical Volume After Stroke study were compared to 39 patients with T2DM. A 3T MRI was undertaken and FreeSurfer v6.0 was used to perform volumetric segmentation of the MR images. Differences in total brain volume (TBV), mean cortical thickness and subcortical volumes were compared. T2DM patients also had an echocardiographic assessment of LVH and 24-hour blood pressure monitoring. Results: Compared to controls, T2DM patients were younger (mean ± SD) 63 ± 7 vs. 68 ± 6 years (p = 0.003), with more obesity (BMI 31 ± 6 vs. 26 ± 4, p < 0.0001) and hypertension (73 vs. 43%, p = 0.013). T2DM patients had significantly more atrophy of the amygdala (p = 0.016), nucleus accumbens (p = 0.001) and brainstem (p = 0.010). Differences remained significant after adjustment for age, education level, total intracranial volume (TIV), BMI and hypertension. T2DM patients with LVH (36%) had more atrophy in the amygdala (p = 0.020) and reduced TBV (p = 0.006) compared to T2DM patients without LVH. Differences in TBV were independent of sex, TIV and education level (p = 0.005). Conclusion: T2DM contributes to accelerated structural brain aging, manifesting as cerebral atrophy. The co-presence of LVH in T2DM may represent a risk factor for subsequent cognitive impairment and dementia.

Smaller hippocampal CA1 subfield volume in posttraumatic stress disorder.

Smaller hippocampal volume in patients with posttraumatic stress disorder (PTSD) represents the most consistently reported structural alteration in the brain. Subfields of the hippocampus play distinct roles in encoding and processing of memories, which are disrupted in PTSD. We examined PTSD-associated alterations in 12 hippocampal subfields in relation to global hippocampal shape, and clinical features. Case-control cross-sectional studies of U.S. military veterans (n=282) from the Iraq and Afghanistan era were grouped into PTSD (n=142) and trauma-exposed controls (n=140). Participants underwent clinical evaluation for PTSD and associated clinical parameters followed by MRI at 3 T. Segmentation with FreeSurfer v6.0 produced hippocampal subfield volumes for the left and right CA1, CA3, CA4, DG, fimbria, fissure, hippocampus-amygdala transition area, molecular layer, parasubiculum, presubiculum, subiculum, and tail, as well as hippocampal meshes. Covariates included age, gender, trauma exposure, alcohol use, depressive symptoms, antidepressant medication use, total hippocampal volume, and MRI scanner model. Significantly lower subfield volumes were associated with PTSD in left CA1 (P=0.01; d=0.21; uncorrected), CA3 (P=0.04; d=0.08; uncorrected), and right CA3 (P=0.02; d=0.07; uncorrected) only if ipsilateral whole hippocampal volume was included as a covariate. A trend level association of L-CA1 with PTSD (F4, 221 =3.32, P=0.07) is present and the other subfield findings are nonsignificant if ipsilateral whole hippocampal volume is not included as a covariate. PTSD-associated differences in global hippocampal shape were nonsignificant. The present finding of smaller hippocampal CA1 in PTSD is consistent with model systems in rodents that exhibit increased anxiety-like behavior from repeated exposure to acute stress. Behavioral correlations with hippocampal subfield volume differences in PTSD will elucidate their relevance to PTSD, particularly behaviors of associative fear learning, extinction training, and formation of false memories.

COMBINATORIAL SENSORY MODALITY ASSESSMENT IN PRODROMAL ALZHEIMER’S DISEASE: RELATION TO MRI AND AMYLOID AND TAU PET

Measures of sensory function, including vision and smell, have been shown to be impaired in Alzheimer's disease (AD), even in prodromal stages. Our goal was to evaluate whether visual function and smell identification combined would provide a better prediction of AD pathology (i.e., cortical amyloid deposition, medial temporal lobe (MTL) tau deposition, MTL atrophy) than either measure alone in a cohort of cognitively normal older adults (CN), older adults with subjective cognitive decline (SCD), and mild cognitive impairment patients (MCI). 34 individuals (14 CN, 11 SCD, 9 MCI) underwent assessments of visual contrast sensitivity (via frequency doubling technology), olfactory identification (the UPSIT), amyloid PET with either [18F]Florbetapir (FBP) or [18F]Florbetaben (FBB), tau PET with [18F]Flortaucipir (FTP), and MRI. Scans were collected using ADNI-2/3 protocols for amyloid PET, tau PET, and MPRAGE (http://www.adni-info.org). Scans were processed using standard techniques to generate smoothed, normalized static images from 50-70min (FBP), 90-110min (FBB), or 80-100min (FTP). Standardized uptake value ratio images (SUVR) were generated using the appropriate reference region (whole cerebellum for FBP, cerebellar grey matter for FBB, cerebellar crus for FTP). SUVR values from the cerebral cortex (for amyloid measures) and the MTL (includes entorhinal cortex, fusiform, and parahippocampal gyri; for FTP) were extracted. Entorhinal cortex (EC) thickness was determined using Freesurfer v5.1. Linear regression models were completed to determine whether contrast sensitivity and UPSIT total score together predict amyloid (z-scored relative to ADNI CN), tau, and atrophy better than either measure alone, covaried for age as needed. In combination, the visual and olfactory measures predicted amyloid, tau, and EC thickness, with poorer performance associated with more amyloid and tau and thinner EC (p<0.05; Table 1, Figure 1A, 2A, 3A). Lower visual contrast sensitivity alone predicted higher tau (p<0.001; Figure 2B), while lower UPSIT score alone predicted a thinner EC (p=0.021, Figure 3C). Relationship of Sensory Measures with Amyloid Deposition Relationship of Sensory Measures with Tau Deposition Relationship of Sensory Measures and Entorhinal Cortex Thickness In general, the combination of both a visual and an olfactory measure showed a better prediction of amyloid, tau, and neurodegeneration. These findings suggest that multi-modal vs single modality sensory assessment may provide a better estimation of AD-related pathology during preclinical and prodromal disease stages.

Concussion History Predicts Reduced Cortical Thickness in Special Operations Forces Personnel

Special Operations Forces (SOF) personnel are at high risk for repetitive blast and head impact exposure. Non-invasive neuroimaging techniques, such as magnetic resonance imaging (MRI), have identified concussion-related structural differences even when observable behavioral and cognitive deficits are absent. The relationship between injury history, neuroimaging, and standard clinical tests has not previously been defined in the SOF population. PURPOSE: To compare cortical morphology, symptom scores, and neurocognition in SOF personnel with and without concussion history. METHODS: SOF personnel completed an assessment battery including 3T high-resolution MRI and the Immediate Postconcussion Assessment and Cognitive Test (ImPACT). We examined symptom reporting and the ImPACT composite scores for verbal and visual memory, visual-motor processing speed, reaction time, and impulse control. The SOF personnel were categorized by self-reported concussion history (no history vs. 1+ concussions). We used FreeSurfer (v6) to reconstruct and segment the cerebral cortex. Cortical thickness was regressed on concussion history controlling for estimated total intracranial volume. The symptom reporting and ImPACT composite scores were regressed on concussion history. RESULTS: We imaged 166 SOF personnel (160 males; 65 self-reported concussion history) using MRI. Of these, 155 completed the ImPACT during a healthy baseline testing session. Two brain regions had reduced cortical thickness associated with concussion history, controlling for the total intracranial volume: left pericalcarine (t164=2.00, p=0.04); and left parahippocampal (t164=2.81, p=0.006). One region had larger cortical thickness in those with a concussion history: right transverse temporal (t164=2.35, p=0.02). Concussion history did not predict symptom or ImPACT composite score differences (p>0.05). CONCLUSIONS: Concussion history predicted cortical thickness in brain regions associated with vision and memory, which are cognitive functions affected following concussive injury. The ImPACT composites were not sensitive to concussion history. Supported by the United States Army Special Operations Command (USASOC)

F50. Genetic Architecture of Hippocampal Subfield Volumes: Shared and Specific Influences

Background The hippocampus is a heterogeneous structure, comprising histologically distinguishable subfields. These subfields are known to be differentially involved in memory consolidation, spatial navigation and pattern separation, complex functions often found to be impaired in individuals with brain disorders associated with reduced hippocampal volume, including Alzheimer's disease (AD) and schizophrenia. Given these structural and functional differences, we sought to characterize the subfields’ shared and specific genetic architecture. Methods T1-images (n= 17418, 16 cohorts) were processed with the hippocampal subfields algorithm in FreeSurfer v6.0. We calculated the SNP-based heritability of 12 subfields, as well as their genetic correlation with each other, with other structural brain features, and with AD and schizophrenia. We further ran a genome-wide association analysis on each subfield, correcting for total hippocampal volume. All analyses included age, age2, sex, and intracranial volume as covariates. Results Volumes of all subfields were heritable (h2 ranging from .15 to .29, all p .41), compared to other brain features. The subiculum and the hippocampal-amygdalar transition area (HATA) showed significant genetic correlation with AD and schizophrenia, respectively. We found 14 independent whole-genome significant loci across six subfields, of which eight had not been previously linked to the hippocampus. Top SNPs were annotated to genes associated with neuronal differentiation, locomotor behaviour, schizophrenia and AD. Conclusions Hippocampal subfields have partly distinct genetic determinants, associated with specific biological processes and traits. Taking into account this specificity may aid in furthering our understanding of hippocampal neurobiology and associated disorders.

O10.5. ABNORMAL MODULAR ORGANIZATION OF THE FUNCTIONAL CONNECTOME PREDICTS CONVERSION TO PSYCHOSIS IN CLINICAL HIGH-RISK YOUTH

BackgroundThe first episode of schizophrenia is typically preceded by a prodromal phase characterized by sub-threshold symptoms and declining functioning. Elucidating the neurobiological substrate of prodromal symptoms that progress into overt psychotic illness is crucial to the development of early detection and intervention strategies for schizophrenia. In this study, we performed a functional connectome analysis in a large group of adolescents and young adults at Clinical High Risk (CHR) for schizophrenia. We aim to assess whether, and if so how, baseline connectome organization distinguishes CHR youth that go on to develop psychosis.MethodsThis study comprises a total of 251 subjects, including 158 psychotropically-naïve CHR subjects (CHRs) and 93 healthy controls (HCs), who were matched to CHRs on age, gender, and level of education. Prodromal symptoms and cognition were assessed using the SIPS structured interview and MATRICS cognitive battery. Anatomical T1 MRI and resting-state fMRI scans were collected at baseline and processed using Freesurfer v6.0 and CONN v17.d software. For each subject, a functional connectome map was reconstructed consisting of 162 nodes representing 148 cortical regions from the Destrieux atlas and 14 subcortical structures. Functional connectomes were analyzed in terms of modular topology using the Louvain community detection method. Modular network partitions of individual CHRs were compared to a group-averaged HC network using the rand similarity coefficient (SR), providing a measure of the level of (ab)normality of the CHRs’ modular partitions. Analysis of covariance (correcting for age- and gender) was used to compare SR levels between CHRs who developed psychosis during follow-up (CHR+; N = 23) as compared to CHRs who did not develop psychosis (CHR-; N = 135). Kaplan-Meier analysis was used to estimate psychosis-free survival functions for CHRs with below- versus above-average SR, which were compared using log-rank tests. Cox regression analysis was used to assess how baseline connectome organization and clinical measures (i.e., demographics, symptoms, IQ) predicted time to conversion.ResultsModular community detection in HCs yielded five major modules including a posterior ‘visual’, central ‘sensorimotor’, medial frontoparietal ‘default-mode’, lateral frontoparietal ‘central-executive’, and inferior ‘limbic’ module. Modular connectome organization of CHR+ was significantly less similar to HCs than CHR- (F(1,154) = 7.14, p = 0.008). A region-specific analysis to identify which regions contributed most to aberrant modular connectome organization in CHR+ showed that superior temporal (including STG), medial temporal (including amygdala), and ventromedial prefrontal regions were most abnormal in terms of their modular assignment. Psychosis-free survival functions of CHRs with low versus high SR were significantly different (z = 2.5, p = 0.013), with a Hazard ratio of 3.3 indicating an over 3-fold relative event rate (i.e., conversion to psychosis) in CHRs with abnormal baseline connectome organization. Cox regression analysis indicated that baseline connectome organization (z = -2.3, p = 0.019), IQ (z = -2.7, p = 0.007), and gender (z = 2.0, p = 0.048) predicted time to conversion.DiscussionThis study indicates that abnormalities in functional connectome organization precede the first psychotic episode. Conversion to psychosis was found to be over three times more likely in CHRs with abnormal modular organization of the functional connectome at baseline. Our results suggest that functional connectome reorganization may underlie the gradual manifestation of prodromal symptoms. These findings may contribute to early diagnosis and intervention in schizophrenia.

S180. MICROSTRUCTURE COMPLEXITY OF THE THALAMUS IN SCHIZOPHRENIA: A NODDI STUDY

BackgroundSchizophrenia is a neurodevelopmental disease arising from complex interactions between genetic and environmental factors that cause disconnectivity within core brain networks including the thalamus. The thalamus has a central role in the pathophysiology of schizophrenia, however to what extent and how it is affected at the microstructural level is still a matter of debate. In the current study, we apply the Neurite Orientation Dispersion and Density Imaging (NODDI) [1], a recently developed MRI technique, which allows the estimation of the microstructural complexity of dendrites and axons in vivo.MethodsTwenty-three patients with schizophrenia (SCHZ) were recruited from the Service of General Psychiatry (Lausanne University Hospital, Switzerland) (40.18 ± 9.2yo; 18/5 males/females) and 27 healthy controls (HC) (37.7 ± 7.95yo; 18/9 males/females). Magnetization-Prepared Rapid Acquisition Gradient Echo (MPRAGE) and a diffusion spectrum imaging (DSI) was performed on a 3-Tesla scanner (MAGNETOM Trio a Tim system, Siemens, Germany). Thalamus segmentation was performed on the MPRAGE sequence with an in house-pipeline using Freesurfer v5.0.0 for segmentation which was then refined to remove voxels within the ventricles and/or overlapping the internal capsule [2]. Orientation Dispersion Index (ODI), Intracellular Volume Fraction (ICVF) and, Isotropic Volume Fraction (ISOVF) were estimated based on the DSI sequence with NODDI [1]. General Linear Models (GLM) were estimated with outcome measures (ICVF, ISOVF, ODI) as dependent variables, group membership as a fixed factor (HC vs. SCHZ) and age and gender as potential covariates.References1Battistella G, Najdenovska E, Maeder P, et al. Robust thalamic nuclei segmentation method based on local diffusion magnetic resonance properties. Brain Struct Funct 2016. DOI:10.1007/s00429-016-1336-4.2Zhang H, Schneider T, Wheeler-kingshott CA, Alexander DC. NeuroImage NODDI : Practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage 2012; 61: 1000–16.ResultsMean ODI was significantly increased in schizophrenia patients compared to controls in the right thalamus (F(1,48)=5.032, p= .030, np2 = .095) and in the left thalamus (F(1,48)=4.500, p= .039, np2 = .086). When controlled for age and gender, the difference remained significant for the right thalamus (F(1, 46) = 4.197, p = .046, np2 = .084) but reduced to trend level for the left thalamus (F(1, 46) = 4.029, p = .051, np2 = .081). There were no significant differences on the other measures (ICVF, ISOVF).DiscussionOur results show that the thalamus is affected in patients with SCHZ at the microstructural level. The observed increase in ODI, which estimates the dispersion of neurite orientations, suggests disrupted neurite organization in patients as compared to HC.

T166. SPATIAL INCOHERENCE OF LARGE-SCALE CORTICAL NETWORKS RELATES TO FORMAL THOUGHT DISORDER IN SCHIZOPHRENIA: A 7T MRI-BASED THICKNESS STUDY

BackgroundThe thickness of cerebral cortex varies across individuals as well as across different regions within an individual. Shared trophic or plastic influences such as repeated task-related recruitment of extant brain regions results in morphological covariance within large-scale brain networks. Pathological processes disrupting functional co-activation can result in higher than expected degree of variability within large-scale networks in an individual level, resulting in spatial incoherence. We studied spatial incoherence of cortical thickness in 17 cortical networks identified on the basis of well-known patterns of intrinsic connectivity, to identify the spatially incoherent networks and relate them to differences in severity of thought disorder among patients with schizophrenia.MethodsUltra-high field 7T anatomical MRI scans (MPRAGE) were obtained from 20 subjects in a clinically stable, medicated early stage of schizophrenia, and 19 sex, parental socioeconomic-status and age matched healthy controls. Cortical thickness was estimated using Freesurfer v5.0, across 17 networks based on the parcellation scheme of Yeo et al. We computed within-network coefficient of variation in thickness (CVT) across vertices that constitute each network. Higher CVT of a network in a subject indicates higher spatial incoherence within the network for that individual. Independent 2-tailed t-tests were used to compare CVT of 17 networks between the 2 groups with FDR-corrected p=0.05 considered as statistically significant. We related CVT of affected networks to the scores of positive and negative Formal Thought Disorder measured using Thought and Language Index in patients.ResultsSalience Network (aka Ventral Attention Network as per Yeo atlas), Default Mode Network and Central Executive Network (aka dorsal Attention Network in Yeo atlas) showed most significant reduction in MRI-derived cortical thickness (networks #8, #12, #15 as well as #16 of Yeo atlas). Only the Salience and Executive Networks (network #8 and #12) showed higher coefficient of variation in patients compared to controls, indicating either a failure of coordinated maturation or co-ordinated function. Higher spatial incoherence of Salience Network related to reduced mean thickness of Central Executive Network in patients with schizophrenia; this relationship was not seen in healthy controls (Fisher’s z test, p=0.02). Both higher coefficient of variation in Salience Network and lower mean thickness in Central Executive Network predicted the severity of positive but not negative thought disorder scores.DiscussionOur results indicate that (1) large-scale cortical networks involved in information processing (Salience and Executive Networks) show spatial incoherence in schizophrenia (2) the degree of spatial incoherence relates to the severity of disorganisation of thoughts and language in patients. Spatial incoherence may be the result of a dysmaturational or functional dysplastic effect reflecting inefficient cortical recruitment in schizophrenia. Within-subject morphological variability carries useful information that can potentially explain the elusive neural basis of complex symptoms such as formal thought disorder.

Test-retest reliability and longitudinal analysis of automated hippocampal subregion volumes in healthy ageing and Alzheimer's disease populations.

The hippocampal formation is a complex brain structure that is important in cognitive processes such as memory, mood, reward processing and other executive functions. Histological and neuroimaging studies have implicated the hippocampal region in neuropsychiatric disorders as well as in neurodegenerative diseases. This highly plastic limbic region is made up of several subregions that are believed to have different functional roles. Therefore, there is a growing interest in imaging the subregions of the hippocampal formation rather than modelling the hippocampus as a homogenous structure, driving the development of new automated analysis tools. Consequently, there is a pressing need to understand the stability of the measures derived from these new techniques. In this study, an automated hippocampal subregion segmentation pipeline, released as a developmental version of Freesurfer (v6.0), was applied to T1-weighted magnetic resonance imaging (MRI) scans of 22 healthy older participants, scanned on 3 separate occasions and a separate longitudinal dataset of 40 Alzheimer's disease (AD) patients. Test-retest reliability of hippocampal subregion volumes was assessed using the intra-class correlation coefficient (ICC), percentage volume difference and percentage volume overlap (Dice). Sensitivity of the regional estimates to longitudinal change was estimated using linear mixed effects (LME) modelling. The results show that out of the 24 hippocampal subregions, 20 had ICC scores of 0.9 or higher in both samples; these regions include the molecular layer, granule cell layer of the dentate gyrus, CA1, CA3 and the subiculum (ICC > 0.9), whilst the hippocampal fissure and fimbria had lower ICC scores (0.73-0.88). Furthermore, LME analysis of the independent AD dataset demonstrated sensitivity to group and individual differences in the rate of volume change over time in several hippocampal subregions (CA1, molecular layer, CA3, hippocampal tail, fissure and presubiculum). These results indicate that this automated segmentation method provides a robust method with which to measure hippocampal subregions, and may be useful in tracking disease progression and measuring the effects of pharmacological intervention.

Effects of HIV Infection, methamphetamine dependence and age on cortical thickness, area and volume.

ObjectiveThis study examined the effects of HIV infection, methamphetamine dependence and their interaction on cortical thickness, area and volume, as well as the potential interactive effects on cortical morphometry of HIV and methamphetamine with age.MethodT1-weighted structural images were obtained on a 3.0T General Electric MR750 scanner. Freesurfer v5.3.0 was used to derive cortical thickness, area and volume measures in thirty-four regions based on Desikan-Killiany atlas labels.ResultsFollowing correction for multiple statistical tests, HIV diagnosis was not significantly related to cortical thickness or area in any ROI, although smaller global cortical area and volume were seen in those with lower nadir CD4 count. HIV diagnosis, nevertheless, was associated with smaller mean cortical volumes in rostral middle frontal gyrus and in the inferior and superior parietal lobes. Methamphetamine dependence was significantly associated with thinner cortex especially in posterior cingulate gyrus, but was not associated with cortical area or volume following correction for multiple statistical tests. We found little evidence that methamphetamine dependence moderated differences in cortical area, volume or thickness for any ROI in the HIV seropositive group. Interactions with age revealed that HIV diagnosis attenuated the degree of age-related cortical thinning seen in non-infected individuals; intercepts indicated that young HIV seropositive individuals had thinner cortex than non-infected peers.ConclusionsMethamphetamine dependence does not appear to potentiate a reduction of cortical area, volume or thickness in HIV seropositive individuals. The finding of thinner cortex in young HIV seropositive individuals and the association between CD4 nadir and global cortical area and volume argue for prioritizing early antiretroviral treatment.

Less Cortical Thickness in Patients With Persistent Post-Traumatic Headache Compared With Healthy Controls: An MRI Study.

To investigate differences in cortical thickness in patients with persistent post-traumatic headache (PPTH) relative to healthy controls and to interrogate whether cortical morphology relates to headache burden (headache frequency, years with post-traumatic headache, PTH) in patients with PPTH. PTHs are one of the most common symptoms following concussion. In some patients, PTHs continue for longer than three months and are classified as PPTH. This study has two main goals: (1) To delineate the neuropathology of PPTH, by interrogating differences in cortical thickness in patients with PPTH relative to healthy controls. (2) To interrogate potential associations between brain morphology and headache burden in patients with PPTH by examining whether cortical thickness relates to frequency of headaches or years lived with PTH. Adults with PPTH diagnosed according to ICHD 3 beta diagnostic criteria and healthy controls underwent brain MRI on a 3 Tesla scanner. Vertex-by-vertex whole brain estimates of cortical thickness were automatically calculated using FreeSurfer v5.3. Differences in cortical thickness in patients with PPTH relative to healthy controls were determined using a general linear model design. Associations were explored between regional clusters where patients with PPTH showed cortical thickness differences compared with healthy controls with headache frequency and years lived with PPTH. This study included 33 patients with PPTH and 33 healthy control subjects (healthy controls: median age = 33.0, IQR = 15.5; patients with PPTH: median age = 36.0, IQR = 20.5; P = .56). Patients with PPTH had less cortical thickness relative to healthy controls in the left and right superior frontal, caudal middle frontal, and precentral cortex as well as less cortical thickness in the right supramarginal, right superior and inferior parietal, and right precuneus region (P < .05, Monte Carlo corrected for multiple comparisons). There were no regions where patients with PPTH had more cortical thickness relative to healthy controls. A correlation analysis of regions that showed less cortical thickness in patients with PPTH demonstrated a negative correlation between left and right superior frontal thickness with headache frequency (P < .05). There was no association between regional cortical thickness and years lived with PPTH. Compared with healthy controls, patients with PPTH had less cortical thickness in bilateral frontal regions and right hemisphere parietal regions. For patients with PPTH, more frequent headaches were related to less thickness in the left and right superior frontal regions, potentially indicating that brain morphology changes in the superior frontal regions in patients with PPTH are modified by headache frequency.

NAPR: a Cloud-Based Framework for Neuroanatomical Age Prediction.

The availability of cloud computing services has enabled the widespread adoption of the "software as a service" (SaaS) approach for software distribution, which utilizes network-based access to applications running on centralized servers. In this paper we apply the SaaS approach to neuroimaging-based age prediction. Our system, named "NAPR" (Neuroanatomical Age Prediction using R), provides access to predictive modeling software running on a persistent cloud-based Amazon Web Services (AWS) compute instance. The NAPR framework allows external users to estimate the age of individual subjects using cortical thickness maps derived from their own locally processed T1-weighted whole brain MRI scans. As a demonstration of the NAPR approach, we have developed two age prediction models that were trained using healthy control data from the ABIDE, CoRR, DLBS and NKI Rockland neuroimaging datasets (total N=2367, age range 6-89years). The provided age prediction models were trained using (i) relevance vector machines and (ii) Gaussian processes machine learning methods applied to cortical thickness surfaces obtained using Freesurfer v5.3. We believe that this transparent approach to out-of-sample evaluation and comparison of neuroimaging age prediction models will facilitate the development of improved age prediction models and allow for robust evaluation of the clinical utility of these methods.

MEDIAL TEMPORAL LOBE TAU IS STRONGLY RELATED TO EPISODIC MEMORY DECLINE IN AGING

The medial temporal lobe (MTL) is the earliest site of tau accumulation. MTL dysfunction may underlie episodic memory decline in normal aging and early dementia. Postmortem data indicate that transentorhinal cortex and hippocampus (HC) tau pathology is common over 60 years-of-age, while spread to inferior temporal (IFT) regions and worsening cognition is associated with beta-amyloid (Aβ). We used PET to investigate how in vivo tau accumulation in temporal lobe regions, Aβ deposition, and MTL atrophy measures contribute to memory and memory decline in cognitively normal older adults. Seventy-five subjects (77±6yrs) underwent [18F]AV-1451 tau PET, [11C]PiB amyloid PET, T1-weighted structural MRI, and neuropsychological assessment (60 with ≥ 2 sessions). T1-images were segmented (FreeSurfer v5.3) to derive volume/thickness measures, a global cortical PiB DVR, and regional AV-1451 measures in native space (80–100min SUVR, cerebellar gray reference, partial volume corrected). Mean SUVRs were derived from MTL and IFT, and temporal lobe subregions (after automated division along the anterior-posterior axis). An episodic memory composite score was derived by averaging Z-transformed verbal and visual early recall, late recall and recognition performance. Changes in memory were estimated by linear-mixed effects models. Stepwise multiple regressions identified the best predictor(s) for memory or memory change, among demographic variables, MTL and IFT tau, global Aβ, HC volume, and entorhinal (EC) thickness. Interactive effects of MTL or IFT tau x Aβ on memory were tested by general linear models. Contributions of temporal lobe subregions were assessed by subsequent regressions. Stepwise regressions identified MTL tau as the best predictor of memory and memory decline (R2=.34 and .33, p<.001). Correlations between MTL tau and memory were seen in both PiB- and PiB+ individuals (Figure). GLMs did not reveal any significant interactive effect of MTL or IFT tau with Aβ on memory. Among temporal lobe subregions, posterior EC and parahippocampal cortex best predicted memory (R2=.32), whereas medial IFT and posterior EC best predicted memory decline (R2=.34). Consistent with neuropathological studies, our data suggest close links between MTL tau pathology (particularly in posterior EC) and age-related memory dysfunction, with stronger decline associated with involvement of IFT regions. Relationship between episodic memory at session closest to AV-1451 tau scan and tau tracer uptake in the posterior EC (PiB- subjects: rPearson=-.43; rSpearman=-.41; PiB+ subjects: rPearson=-.52; rSpearman=-.54; all p<.001). A similar relationship was seen between memory and whole MTL mean SUVR, which we defined as average across Freesurfer-defined EC, para-hippocampal gyrus, hippocampus and amygdala. Posterior EC was defined here as the part of EC/parahippocampal gyrus (Freesurfer nomenclature) that covers the same slices as the anterior half of the hippocampus.

STRUCTURAL CORRELATES OF ALZHEIMER’S DISEASE AND AGING IN DOWN SYNDROME: AN MRI STUDY

Down syndrome (DS) has been proposed as a genetic form of Alzheimer's Disease (AD). However, few studies had assessed the structural correlates of AD and aging in DS. Our objective was to evaluate the brain regions vulnerable to AD and aging in DS and the possible modulating effects of amyloid deposition. Subjects were recruited from the Down Alzheimer Barcelona Neuroimaging Initiative (DABNI), a longitudinal cohort to study AD in DS. 68 subjects underwent a 3 Tesla structural MRI and a lumbar puncture (mean age: 41.32, 34.1% female). DS subjects were classified into a cognitive stable group (cs-DS, N=52) and an AD group (n=16). The AD group was subdivided into prodomal Alzheimer (p-DS, N=10) and AD dementia (d-DS, N=6) subgroups. CSF Aß1–42 levels were measured by ELISA. Subjects were classified as Aß+ or Aß- (cutoff <550pg/ml). Cortical thickness (CTh) and normalized hippocampal volume (HV) were obtained using Freesurfer v5.1. We computed the mean CTh in a well-known AD signature map (Dickerson 2009). We performed group comparissons and correlation analyses. We also performed an interaction analysis between age and CSF Aß1–42 to show the regions with an amyloid by age interaction on CTh. The AD group had extensive regions of cortical atrophy in posterior temporoparietal areas with a relative preservation of frontal areas when compared with s-DS. There was a significant gradient of atrophy in the AD signature and HV across groups (d-DS>p-DS>cs-DS). Aging negatively correlated CTh in cs-DS in regions of the frontal cortex. This correlation was also significant for HV but not for the AD signature. CSF Aß1–42 did not correlate with CTh and there were no differences in the Aß+ vs Aß- comparison in cs-DS. However, we found a significant interaction between CSF Aß1–42 levels and age on brain structure in posterior regions.