A comparison of FreeSurfer-generated data with and without manual intervention

Network Diffusion Modeling Explains Longitudinal Tau PET Data

Alzheimer's disease is associated with the cerebral accumulation of neurofibrillary tangles of hyperphosphorylated tau protein. The progressive occurrence of tau aggregates in different brain regions is closely related to neurodegeneration and cognitive impairment. However, our current understanding of tau propagation relies almost exclusively on postmortem histopathology, and the precise propagation dynamics of misfolded tau in the living brain remain poorly understood. Here we combine longitudinal positron emission tomography and dynamic network modeling to test the hypothesis that misfolded tau propagates preferably along neuronal connections. We follow 46 subjects for three or four annual positron emission tomography scans and compare their pathological tau profiles against brain network models of intracellular and extracellular spreading. For each subject, we identify a personalized set of model parameters that characterizes the individual progression of pathological tau. Across all subjects, the mean protein production rate was 0.21 ± 0.15 and the intracellular diffusion coefficient was 0.34 ± 0.43. Our network diffusion model can serve as a tool to detect non-clinical symptoms at an earlier stage and make informed predictions about the timeline of neurodegeneration on an individual personalized basis.

Cortical Thickness Estimation in Individuals With Cerebral Small Vessel Disease, Focal Atrophy, and Chronic Stroke Lesions

BACKGROUND

Regional changes to cortical thickness in individuals with neurodegenerative and cerebrovascular diseases (CVD) can be estimated using specialized neuroimaging software. However, the presence of cerebral small vessel disease, focal atrophy, and cortico-subcortical stroke lesions, pose significant challenges that increase the likelihood of misclassification errors and segmentation failures.

PURPOSE

The main goal of this study was to examine a correction procedure developed for enhancing FreeSurfer's (FS's) cortical thickness estimation tool, particularly when applied to the most challenging MRI obtained from participants with chronic stroke and CVD, with varying degrees of neurovascular lesions and brain atrophy.

METHODS

In 155 CVD participants enrolled in the Ontario Neurodegenerative Disease Research Initiative (ONDRI), FS outputs were compared between a fully automated, unmodified procedure and a corrected procedure that accounted for potential sources of error due to atrophy and neurovascular lesions. Quality control (QC) measures were obtained from both procedures. Association between cortical thickness and global cognitive status as assessed by the Montreal Cognitive Assessment (MoCA) score was also investigated from both procedures.

RESULTS

Corrected procedures increased "Acceptable" QC ratings from 18 to 76% for the cortical ribbon and from 38 to 92% for tissue segmentation. Corrected procedures reduced "Fail" ratings from 11 to 0% for the cortical ribbon and 62 to 8% for tissue segmentation. FS-based segmentation of T1-weighted white matter hypointensities were significantly greater in the corrected procedure (5.8 mL vs. 15.9 mL, p < 0.001). The unmodified procedure yielded no significant associations with global cognitive status, whereas the corrected procedure yielded positive associations between MoCA total score and clusters of cortical thickness in the left superior parietal (p = 0.018) and left insula (p = 0.04) regions. Further analyses with the corrected cortical thickness results and MoCA subscores showed a positive association between left superior parietal cortical thickness and Attention (p < 0.001).

CONCLUSION

These findings suggest that correction procedures which account for brain atrophy and neurovascular lesions can significantly improve FS's segmentation results and reduce failure rates, thus maximizing power by preventing the loss of our important study participants. Future work will examine relationships between cortical thickness, cerebral small vessel disease, and cognitive dysfunction due to neurodegenerative disease in the ONDRI study.

The Mid-Fusiform Sulcus in Autism Spectrum Disorder: Establishing a Novel Anatomical Landmark Related to Face Processing

Despite decades of research, the brain basis of aberrant face processing in autism spectrum disorder (ASD) remains a topic of debate. The mid-fusiform sulcus (MFS), a minor feature of the ventral occipitotemporal cortex, provides new directions for studying face processing. The MFS closely aligns with face-selective cortical patches and other structural and functional divisions of the fusiform gyrus; however, it has received little attention in clinical populations. We collected structural MRI data from 54 individuals with ASD and 61 age-and-IQ-matched controls ages 8 to 40 years. The MFS was identified on cortical surface reconstructions via 4 trained raters and classified into known surface patterns. Mean MFS gray matter volume (GMV), cortical surface area (SA), cortical thickness (CT), and standard deviation of CT (CT SD) were extracted. Effects of diagnosis, age, and hemisphere on MFS surface presentation and morphometry were assessed via multinomial logistic regression and mixed effects general linear modeling, respectively. The MFS was reliably identified in 97% of hemispheres examined. Macroanatomical patterns and age-related decreases in MFS GMV and CT were similar between groups. CT SD was greater in the left hemisphere in ASD. Participants' ability to interpret emotions and mental states from facial features was significantly negatively correlated with MFS CT and CT SD. Overall, the MFS is a stable feature of the fusiform gyrus in ASD and CT related measures appear to be sensitive to diagnosis and behavior. These results can inform future investigations of face processing and structure-function relationships in populations with social deficits. LAY SUMMARY: A small structural feature of the brain related to seeing faces (the mid-fusiform sulcus; MFS) appears similar in autism spectrum disorder (ASD) and neurotypical development; however, the thickness of this structure on the left side of the brain is more variable in ASD. People who are better at judging mental states from another person's eyes tend to have thinner and less variable MFS. This feature may teach us more about face processing and how brain structure influences function in ASD.

Assessment of White Matter Integrity after Pediatric Traumatic Brain Injury

White matter (WM) abnormalities, such as atrophy and hyperintensities (WMH), can be accessed via magnetic resonance imaging (MRI) after pediatric traumatic brain injury (TBI). Several methods are available to classify WM abnormalities (i.e., total WM volumes and WMHs), but automated and manual volumes and clinical ratings have yet to be compared in pediatric TBI. In addition, WM integrity has been associated reliably with processing speed. Consequently, methods of assessing WM integrity should relate to processing speed to have clinical application. This study had two goals: (1) to compare Scheltens rating scale, manual tracing, FreeSurfer, and NeuroQuant® methods of assessing WM abnormalities, and (2) to relate WM methods to processing speed scores. We report findings from the Social Outcomes of Brain Injury in Kids (SOBIK) study, a multi-center study of 60 children with chronic TBI (65% male) from ages 8-13. Scheltens WMH ratings had good to excellent agreement with WMH volumes for both NeuroQuant (ICC = 0.62; r = 0.29, p = 0.005) and manual tracing (ICC = 0.82; r = 0.50, p = 0.000). NeuroQuant WMH volumes did not correlate with manually traced WMH volumes (r = 0.12, p = 0.21) and had poor agreement (ICC = 0.24). NeuroQuant and FreeSurfer total WM volumes correlated (r = 0.38, p = 0.004) and had fair agreement (ICC = 0.52). The WMH assessment methods, both ratings and volumes, were associated with processing speed scores. In contrast, total WM volume was not related to processing speed. Measures of WMH may hold clinical utility for predicting cognitive functioning after pediatric TBI.

Prospective study of the association between sport-related concussion and brain morphometry (3T-MRI) in collegiate athletes: study from the NCAA-DoD CARE Consortium

OBJECTIVES

To determine the acute and early long-term associations of sport-related concussion (SRC) and subcortical and cortical structures in collegiate contact sport athletes.

METHODS

Athletes with a recent SRC (n=99) and matched contact (n=91) and non-contact sport controls (n=95) completed up to four neuroimaging sessions from 24 to 48 hours to 6 months postinjury. Subcortical volumes (amygdala, hippocampus, thalamus and dorsal striatum) and vertex-wise measurements of cortical thickness/volume were computed using FreeSurfer. Linear mixed-effects models examined the acute and longitudinal associations between concussion and structural metrics, controlling for intracranial volume (or mean thickness) and demographic variables (including prior concussions and sport exposure).

RESULTS

There were significant group-dependent changes in amygdala volumes across visits (p=0.041); this effect was driven by a trend for increased amygdala volume at 6 months relative to subacute visits in contact controls, with no differences in athletes with SRC. No differences were observed in any cortical metric (ie, thickness or volume) for primary or secondary analyses.

CONCLUSION

A single SRC had minimal associations with grey matter structure across a 6-month time frame.

Decline Variability of Cortical and Subcortical Regions in Aging: A Longitudinal Study

Describing the trajectories of age-related change for different brain structures has been of interest in many recent studies. However, our knowledge regarding these trajectories and their associations is still limited due to small sample sizes and low numbers of repeated measures. For the present study, we used a large longitudinal dataset (four measurements over 4 years) comprising anatomical data from a sample of healthy older adults (N = 231 at baseline). This dataset enables us to gain new insights about volumetric cortical and subcortical changes and their associations in the context of healthy aging. Brain structure volumes were derived from T1-weighted MRI scans using FreeSurfer segmentation tools. Brain structure trajectories were fitted using mixed models and latent growth curve models to gain information about the mean extent and variability of decline trajectories for different brain structures as well as the associations between individual trajectories. On the group level, our analyses indicate similar linear changes for frontal and parietal brain regions, while medial temporal regions showed an accelerated decline with advancing age. Regarding subcortical regions, some structures showed strong declines (e.g., hippocampus), others showed little decline (e.g., pallidum). Our data provide little evidence for sex differences regarding the aforementioned trajectories. Between-person variability of the person-specific slopes (random slopes) was largest in subcortical and medial temporal brain structures. When looking at the associations between the random slopes from each brain structure, we found that the decline is largely homogenous across the majority of cortical brain structures. In subcortical and medial temporal brain structures, however, more heterogeneity of the decline was observed, meaning that the extent of the decline in one structure is less predictive of the decline in another structure. Taken together, our study contributes to enhancing our understanding of structural brain aging by demonstrating (1) that average volumetric change differs across the brain and (2) that there are regional differences with respect to between-person variability in the slopes. Moreover, our data suggest (3) that random slopes are highly correlated across large parts of the cerebral cortex but (4) that some brain regions (i.e., medial temporal regions) deviate from this homogeneity.

Automatic Segmentation of the Dorsal Claustrum in Humans Using in vivo High-Resolution MRI

The claustrum is a thin sheet of neurons enclosed by white matter and situated between the insula and the putamen. It is highly interconnected with sensory, frontal, and subcortical regions. The deep location of the claustrum, with its fine structure, has limited the degree to which it could be studied in vivo. Particularly in humans, identifying the claustrum using magnetic resonance imaging (MRI) is extremely challenging, even manually. Therefore, automatic segmentation of the claustrum is an invaluable step toward enabling extensive and reproducible research of the anatomy and function of the human claustrum. In this study, we developed an automatic algorithm for segmenting the human dorsal claustrum in vivo using high-resolution MRI. Using this algorithm, we segmented the dorsal claustrum bilaterally in 1068 subjects of the Human Connectome Project Young Adult dataset, a publicly available high-resolution MRI dataset. We found good agreement between the automatic and manual segmentations performed by 2 observers in 10 subjects. We demonstrate the use of the segmentation in analyzing the covariation of the dorsal claustrum with other brain regions, in terms of macro- and microstructure. We identified several covariance networks associated with the dorsal claustrum. We provide an online repository of 1068 bilateral dorsal claustrum segmentations.

A comparison of Freesurfer and multi-atlas MUSE for brain anatomy segmentation: Findings about size and age bias, and inter-scanner stability in multi-site aging studies

Automatic segmentation of brain anatomy has been a key processing step in quantitative neuroimaging analyses. An extensive body of literature has relied on Freesurfer segmentations. Yet, in recent years, the multi-atlas segmentation framework has consistently obtained results with superior accuracy in various evaluations. We compared brain anatomy segmentations from Freesurfer, which uses a single probabilistic atlas strategy, against segmentations from Multi-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters and locally optimal atlas selection (MUSE), one of the leading ensemble-based methods that calculates a consensus segmentation through fusion of anatomical labels from multiple atlases and registrations. The focus of our evaluation was twofold. First, using manual ground-truth hippocampus segmentations, we found that Freesurfer segmentations showed a bias towards over-segmentation of larger hippocampi, and under-segmentation in older age. This bias was more pronounced in Freesurfer-v5.3, which has been used in multiple previous studies of aging, while the effect was mitigated in more recent Freesurfer-v6.0, albeit still present. Second, we evaluated inter-scanner segmentation stability using same day scan pairs from ADNI acquired on 1.5T and 3T scanners. We also found that MUSE obtains more consistent segmentations across scanners compared to Freesurfer, particularly in the deep structures.

Cortical thickness and formal thought disorder in schizophrenia: An ultra high-field network-based morphometry study

BACKGROUND

Persistent formal thought disorder (FTD) is a core feature of schizophrenia. Recent cognitive and neuroimaging studies indicate a distinct mechanistic pathway underlying the persistent positive FTD (pFTD or disorganized thinking), though its structural determinants are still elusive. Using network-based cortical thickness estimates from ultra-high field 7-Tesla Magnetic Resonance Imaging (7T MRI), we investigated the structural correlates of pFTD.

METHODS

We obtained speech samples and 7T MRI anatomical scans from medicated clinically stable patients with schizophrenia (n = 19) and healthy controls (n = 20). Network-based morphometry was used to estimate the mean cortical thickness of 17 functional networks covering the entire cortical surface from each subject. We also quantified the vertexwise variability of thickness within each network to quantify the spatial coherence of the 17 networks, estimated patients vs. controls differences, and related the thickness of the affected networks to the severity of pFTD.

RESULTS

Patients had reduced thickness of the frontoparietal and default mode networks, and reduced spatial coherence affecting the salience and the frontoparietal control network. A higher burden of positive FTD related to reduced frontoparietal thickness and reduced spatial coherence of the salience network. The presence of positive FTD, but not its severity, related to the reduced thickness of the language network comprising of the superior temporal cortex.

CONCLUSIONS

These results suggest that cortical thickness of both cognitive control and language networks underlie the positive FTD in schizophrenia. The structural integrity of cognitive control networks is a critical determinant of the expressed severity of persistent FTD in schizophrenia.

Cingulate cortex function and multi-modal connectivity mapped using intracranial stimulation

The cingulate cortex is part of the limbic system. Its function and connectivity are organized in a rostro-caudal and ventral-dorsal manner which was addressed by various other studies using rather coarse cortical parcellations. In this study, we aim at describing its function and connectivity using invasive recordings from patients explored for focal drug-resistant epilepsy. We included patients that underwent stereo-electroencephalographic recordings using intracranial electrodes in the University Emergency Hospital Bucharest between 2012 and 2019. We reviewed all high frequency stimulations (50 ​Hz) performed for functional mapping of the cingulate cortex. We used two methods to characterize brain connectivity. Effective connectivity was inferred based on the analysis of cortico-cortical potentials (CCEPs) evoked by single pulse electrical stimulation (SPES) (15 ​s inter-pulse interval). Functional connectivity was estimated using the non-linear regression method applied to 60 ​s spontaneous electrical brain signal intervals. The effective (stimulation-evoked) and functional (non-evoked) connectivity analyses highlight brain networks in a different way. While non-evoked connectivity evidences areas having related activity, often in close proximity to each other, evoked connectivity highlights spatially extended networks. To highlight in a comprehensive way the cingulate cortex's network, we have performed a bi-modal connectivity analysis that combines the resting-state broadband h² non-linear correlation with cortico-cortical evoked potentials. We co-registered the patient's anatomy with the fsaverage FreeSurfer template to perform the automatic labeling based on HCP-MMP parcellation. At a group level, connectivity was estimated by averaging responses over stimulated/recorded or recorded sites in each pair of parcels. Finally, for multiple regions that evoked a clinical response during high frequency stimulation, we combined the connectivity of individual pairs using maximum intensity projection. Connectivity was assessed by applying SPES on 2094 contact pairs and recording CCEPs on 3580 contacts out of 8582 contacts of 660 electrodes implanted in 47 patients. Clinical responses elicited by high frequency stimulations in 107 sites (pairs of contacts) located in the cingulate cortex were divided in 10 groups: affective, motor behavior, motor elementary, versive, speech, vestibular, autonomic, somatosensory, visual and changes in body perception. Anterior cingulate cortex was shown to be connected to the mesial temporal, orbitofrontal and prefrontal cortex. In the middle cingulate cortex, we located affective, motor behavior in the anterior region, and elementary motor and somatosensory in the posterior part. This region is connected to the prefrontal, premotor and primary motor network. Finally, the posterior cingulate was shown to be connected with the visual areas, mesial and lateral parietal and temporal cortex.

Interaction effect of alcohol consumption and Alzheimer disease polygenic risk score on the brain cortical thickness of cognitively normal subjects

Alcohol consumption and genetic risk for Alzheimer disease (AD) are among many factors known to be associated with brain structure in cognitively healthy adults. It is unclear, however, whether the effect of alcohol consumption on brain structure varies depending on a person's level of genetic risk for AD. We hypothesized that there is an interaction effect of alcohol consumption and a 33-SNP AD polygenic risk score (PRS) on the cortical thickness of brain regions known to be affected early in the course of AD. Studying 6,213 cognitively healthy subjects from the UK Biobank, we found a significant interaction effect of the 33-SNP AD PRS and alcohol consumption on this AD Cortical Thickness Signature. Stratified, among those who consume 12-24 g/day of alcohol, the 33-SNP AD PRS had a significant, positive association with AD Cortical Thickness Signature, with high-risk subjects having the greatest AD Cortical Thickness Signature. There were no significant associations of the 33-SNP AD PRS with AD Cortical Thickness Signature among the nondrinker or <1, 1-6, 6-12, 24-48, or >48 g/day groups. It is unclear whether this interaction is due to a detrimental or beneficial effect of moderate alcohol consumption in those with the highest genetic risk for AD.

Fitness Level Influences White Matter Microstructure in Postmenopausal Women

Aerobic exercise has both neuroprotective and neurorehabilitative benefits. However, the underlying mechanisms are not fully understood and need to be investigated, especially in postmenopausal women, who are at increased risk of age-related disorders such as Alzheimer’s disease and stroke. To advance our understanding of the potential neurological benefits of aerobic exercise in aging women, we examined anatomical and functional responses that may differentiate women of varying cardiorespiratory fitness using neuroimaging and neurophysiology. A total of 35 healthy postmenopausal women were recruited (59 ± 3 years) and cardiorespiratory fitness estimated (22–70 mL/kg/min). Transcranial magnetic stimulation was used to assess -aminobutyric acid (GABA) and glutamate (Glu) receptor function in the primary motor cortex (M1), and magnetic resonance spectroscopy (MRS) was used to quantify GABA and Glu concentrations in M1. Magnetic resonance imaging was used to assess mean cortical thickness (MCT) of sensorimotor and frontal regions, while the microstructure of sensorimotor and other white matter tracts was evaluated through diffusion tensor imaging. Regression analysis revealed that higher fitness levels were associated with improved microstructure in pre-motor and sensory tracts, and the hippocampal cingulum. Fitness level was not associated with MCT, MRS, or neurophysiology measures. These data indicate that, in postmenopausal women, higher cardiorespiratory fitness is linked with preserved selective white matter microstructure, particularly in areas that influence sensorimotor control and memory.

Independent Multiple Factor Association Analysis for Multiblock Data in Imaging Genetics

Multivariate methods have the potential to better capture complex relationships that may exist between different biological levels. Multiple Factor Analysis (MFA) is one of the most popular methods to obtain factor scores and measures of discrepancy between data sets. However, singular value decomposition in MFA is based on PCA, which is adequate only if the data is normally distributed, linear or stationary. In addition, including strongly correlated variables can overemphasize the contribution of the estimated components. In this work, we introduced a novel method referred as Independent Multifactorial Analysis (ICA-MFA) to derive relevant features from multiscale data. This method is an extended implementation of MFA, where the component value decomposition is based on Independent Component Analysis. In addition, ICA-MFA incorporates a predictive step based on an Independent Component Regression. We evaluated and compared the performance of ICA-MFA with both, the MFA method and traditional univariate analyses, in a simulation study. We showed how ICA-MFA explained up to 10-fold more variance than MFA and univariate methods. We applied the proposed algorithm in a study of 4057 individuals belonging to the population-based Rotterdam Study with available genetic and neuroimaging data, as well as information about executive cognitive functioning. Specifically, we used ICA-MFA to detect relevant genetic features related to structural brain regions, which in turn were involved, in the mechanisms of executive cognitive function. The proposed strategy makes it possible to determine the degree to which the whole set of genetic and/or neuroimaging markers contribute to the variability of the symptomatology jointly, rather than individually. While univariate results and MFA combinations only explained a limited proportion of variance (less than 2%), our method increased the explained variance (10%) and allowed the identification of significant components that maximize the variance explained in the model. The potential application of the ICA-MFA algorithm constitutes an important aspect of integrating multivariate multiscale data, specifically in the field of Neurogenetics.

Magnetic resonance imaging volumetric analysis in patients with Alternating hemiplegia of childhood: A pilot study

Quantitative MRI is increasingly being used as a biomarker in neurological disorders. Cerebellar atrophy occurs in some Alternating Hemiplegia of Childhood (AHC) patients. However, it is not known if cerebellar atrophy can be a potential biomarker in AHC or if quantitative MRI is a reliable method to address this question. Here we determine the reproducibility of an MRI-volumetrics method to investigate brain volumes in AHC and apply it to a population of 14 consecutive AHC patients (ages 4-11 years). We studied method reproducibility in the first 11 patients and then performed correlation of cerebellar volumes, relative to published normal population means, with age in all 14. We used FreeSurfer 6.0.0 to automatically segment MRI images, then performed manual resegmentation correction by two different observers. No significant differences were observed in any of ten brain regions between the two reviewers: p > .591 and interclass Correlation Coefficient (ICC) ≥0.975 in all comparisons. Additionally, there were no significant differences between the means of the two reviewers and the automatic segmentation values: p ≥ .106 and ICC ≥0.994 in all comparisons. We found a negative correlation between cerebellar volume and age (R = -0.631, p = .037), even though only one patient showed any cerebellar atrophy upon formal readings of the MRIs by neuroradiology. Sample size did not allow us to rule out potential confounding variables. Thus, findings from this cross-sectional study should be considered as exploratory. Our study supports the prospective investigation of quantitative MRI-volumetrics of the cerebellum as a potential biomarker in AHC.

Investigating the Added Value of FreeSurfer's Manual Editing Procedure for the Study of the Reading Network in a Pediatric Population

Insights into brain anatomy are important for the early detection of neurodevelopmental disorders, such as dyslexia. FreeSurfer is one of the most frequently applied automatized software tools to study brain morphology. However, quality control of the outcomes provided by FreeSurfer is often ignored and could lead to wrong statistical inferences. Additional manual editing of the data may be a solution, although not without a cost in time and resources. Past research in adults on comparing the automatized method of FreeSurfer with and without additional manual editing indicated that although editing may lead to significant differences in morphological measures between the methods in some regions, it does not substantially change the sensitivity to detect clinical differences. Given that automated approaches are more likely to fail in pediatric-and inherently more noisy-data, we investigated in the current study whether FreeSurfer can be applied fully automatically or additional manual edits of T1-images are needed in a pediatric sample. Specifically, cortical thickness and surface area measures with and without additional manual edits were compared in six regions of interest (ROIs) of the reading network in 5-to-6-year-old children with and without dyslexia. Results revealed that additional editing leads to statistical differences in the morphological measures, but that these differences are consistent across subjects and that the sensitivity to reveal statistical differences in the morphological measures between children with and without dyslexia is not affected, even though conclusions of marginally significant findings can differ depending on the method used. Thereby, our results indicate that additional manual editing of reading-related regions in FreeSurfer has limited gain for pediatric samples.

Hub disruption in patients with chronic neck pain: a graph analytical approach

Chronic pain is known to alter the brain's network dynamics. These dynamics are often demonstrated by identifying alterations in the brain network topology. A common approach used for this purpose is graph theory. To date, little is known on how these potentially altered networks in chronic pain relate to the symptoms reported by these patients. Here, we applied a graph theoretical approach to identify network changes in patients suffering from chronic neck pain, a group that is often neglected in chronic pain research. Participants with chronic traumatic and nontraumatic neck pain were compared to healthy pain-free controls. They showed higher levels of self-reported symptoms of sensitization, higher levels of disability, and impaired sensorimotor control. Furthermore, the brain suffering from chronic neck pain showed altered network properties in the posterior cingulate cortex, amygdala, and pallidum compared with the healthy pain-free brain. These regions have been identified as brain hubs (ie, regions that are responsible for orchestrating communication between other brain regions) and are therefore known to be more vulnerable in brain disorders including chronic pain. We were furthermore able to uncover associations between these altered brain network properties and the symptoms reported by patients. Our findings indicate that chronic neck pain patients reflect brain network alterations and that targeting the brain in patients might be of utmost importance.

Regional brain volumetric changes despite 2 years of treatment initiated during acute HIV infection

OBJECTIVE

To assess changes in regional brain volumes after 24 months among individuals who initiated combination antiretroviral therapy (cART) within weeks of HIV exposure.

DESIGN

Prospective cohort study of Thai participants in the earliest stages of HIV-1infection.

METHODS

Thirty-four acutely HIV-infected individuals (AHI; Fiebig I-V) underwent brain magnetic resonance (MR) imaging and MR spectroscopy at 1.5 T and immediately initiated cART. Imaging was repeated at 24 months. Regional brain volumes were quantified using FreeSurfer's longitudinal pipeline. Voxel-wise analyses using tensor-based morphometry (TBM) were conducted to verify regional assessments. Baseline brain metabolite levels, blood and cerebrospinal fluid biomarkers assessed by ELISA, and peripheral blood monocyte phenotypes measured by flow cytometry were examined as predictors of significant volumetric change.

RESULTS

Participants were 31 ± 8 years old. The estimated mean duration of infection at cART initiation was 15 days. Longitudinal analyses revealed reductions in volumes of putamen (P < 0.001) and caudate (P = 0.006). TBM confirmed significant atrophy in the putamen and caudate, and also in thalamic and hippocampal regions. In exploratory post-hoc analyses, higher baseline frequency of P-selectin glycoprotein ligand-1 (PSGL-1)-expressing total monocytes correlated with greater caudate volumetric decrease (ρ = 0.67, P = 0.017), whereas the baseline density of PSGL-1-expressing inflammatory (CD14CD16) monocytes correlated with putamen atrophy (ρ = 0.65, P = 0.022).

CONCLUSION

Suppressive cART initiated during AHI may not prevent brain atrophy. Volumetric decrease appears greater than expected age-related decline, although examination of longitudinal change in demographically similar HIV-uninfected Thai individuals is needed. Mechanisms underlying progressive HIV-related atrophy may include early activation and enhanced adhesive and migratory capacity of circulating monocyte populations.

Identifying errors in Freesurfer automated skull stripping and the incremental utility of manual intervention

Quality assurance (QA) is vital for ensuring the integrity of processed neuroimaging data for use in clinical neurosciences research. Manual QA (visual inspection) of processed brains for cortical surface reconstruction errors is resource-intensive, particularly with large datasets. Several semi-automated QA tools use quantitative detection of subjects for editing based on outlier brain regions. There were two project goals: (1) evaluate the assumption that statistical outliers are related to errors of cortical extension, and (2) examine whether error identification and correction significantly impacts estimation of cortical parameters and established brain-behavior relationships. T1 MPRAGE images (N = 530) of healthy adults were obtained from the NKI-Rockland Sample and reconstructed using Freesurfer 5.3. Visual inspection of T1 images was conducted for: (1) participants (n = 110) with outlier values (z scores ±3 SD) for subcortical and cortical segmentation volumes (outlier group), and (2) a random sample of remaining participants (n = 110) with segmentation values that did not meet the outlier criterion (non-outlier group). The outlier group had 21% more participants with visual inspection-identified errors than participants in the non-outlier group, with a medium effect size (Φ = 0.22). Nevertheless, a considerable portion of images with errors of cortical extension were found in the non-outlier group (41%). Although nine brain regions significantly changed size from pre- to post-editing (with effect sizes ranging from 0.26 to 0.59), editing did not substantially change the correlations of neurocognitive tasks and brain volumes (ps > 0.05). Statistically-based QA, although less resource intensive, is not accurate enough to supplant visual inspection. We discuss practical implications of our findings to guide resource allocation decisions for image processing.

Structural Brain Alterations in Youth With Psychosis and Bipolar Spectrum Symptoms

OBJECTIVE

Adults with established diagnoses of serious mental illness (bipolar disorder and schizophrenia) exhibit structural brain abnormalities, yet less is known about how such abnormalities manifest earlier in development.

METHOD

Cross-sectional data publicly available from the Philadelphia Neurodevelopmental Cohort (PNC) were analyzed. Structural magnetic resonance neuroimaging data were collected on a subset of the PNC (N = 989; 9-22 years old). Cortical thickness, surface area (SA), and subcortical volumes were calculated. Study participants were assessed for psychiatric symptomatology using a structured interview and the following groups were created: typically developing (n = 376), psychosis spectrum (PS; n = 113), bipolar spectrum (BP; n = 117), and BP + PS (n = 109). Group and developmental differences in structural magnetic resonance neuroimaging measures were examined. In addition, the extent to which any structural aberration was related to neurocognition, global functioning, and clinical symptomatology was examined.

RESULTS

Compared with other groups, PS youth exhibited significantly decreased SA in the orbitofrontal, cingulate, precentral, and postcentral regions. PS youth also exhibited deceased thalamic volume compared with all other groups. The strongest effects for precentral and posterior cingulate SA decreases were seen during early adolescence (13-15 years old) in PS youth. The strongest effects for decreases in thalamic volume and orbitofrontal and postcentral SA were observed in mid-adolescence (16-18 years) in PS youth. Across groups, better overall functioning was associated with increased lateral orbitofrontal SA. Increased postcentral SA was associated with better executive cognition and less severe negative symptoms in the entire sample.

CONCLUSION

In a community-based sample, decreased cortical SA and thalamic volume were present early in adolescent development in youth with PS symptoms, but not in youth with BP symptoms or with BP and PS symptoms. These findings point to potential biological distinctions between PS and BP conditions, which could suggest additional biomarkers relevant to early identification.

Fractal dimension of cerebral white matter: A consistent feature for prediction of the cognitive performance in patients with small vessel disease and mild cognitive impairment

Patients with cerebral small vessel disease (SVD) frequently show decline in cognitive performance. However, neuroimaging in SVD patients discloses a wide range of brain lesions and alterations so that it is often difficult to understand which of these changes are the most relevant for cognitive decline. It has also become evident that visually-rated alterations do not fully explain the neuroimaging correlates of cognitive decline in SVD. Fractal dimension (FD), a unitless feature of structural complexity that can be computed from high-resolution T₁-weighted images, has been recently applied to the neuroimaging evaluation of the human brain. Indeed, white matter (WM) and cortical gray matter (GM) exhibit an inherent structural complexity that can be measured through the FD.

In our study, we included 64 patients (mean age ± standard deviation, 74.6 ± 6.9, education 7.9 ± 4.2 years, 53% males) with SVD and mild cognitive impairment (MCI), and a control group of 24 healthy subjects (mean age ± standard deviation, 72.3 ± 4.4 years, 50% males). With the aim of assessing whether the FD values of cerebral WM (WM FD) and cortical GM (GM FD) could be valuable structural predictors of cognitive performance in patients with SVD and MCI, we employed a machine learning strategy based on LASSO (least absolute shrinkage and selection operator) regression applied on a set of standard and advanced neuroimaging features in a nested cross-validation (CV) loop. This approach was aimed at 1) choosing the best predictive models, able to reliably predict the individual neuropsychological scores sensitive to attention and executive dysfunctions (prominent features of subcortical vascular cognitive impairment) and 2) identifying a features ranking according to their importance in the model through the assessment of the out-of-sample error.

For each neuropsychological test, using 1000 repetitions of LASSO regression and 5000 random permutations, we found that the statistically significant models were those for the Montreal Cognitive Assessment scores (p-value = .039), Symbol Digit Modalities Test scores (p-value = .039), and Trail Making Test Part A scores (p-value = .025). Significant prediction of these scores was obtained using different sets of neuroimaging features in which the WM FD was the most frequently selected feature.

In conclusion, we showed that a machine learning approach could be useful in SVD research field using standard and advanced neuroimaging features. Our study results raise the possibility that FD may represent a consistent feature in predicting cognitive decline in SVD that can complement standard imaging.

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White matter fractal dimension is altered in small vessel disease patients with MCI.

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White matter complexity's decrease consistently predicts worse cognitive performance.

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Fractal dimension may be a new marker of white matter damage in small vessel disease.

A preliminary study of cortical morphology in schizophrenia patients with a history of violence

Clinical studies of patients with schizophrenia and a history of violence are challenging both from an ethical and practical perspective, and the neurobiological underpinnings remain largely unknown. We here present a comprehensive account of the brain cortical characteristics associated with violence in schizophrenia. We obtained 3T MRI scans and thorough clinical characterization of schizophrenia patients with a history of violence (murder, attempted murder, criminal assault, SCZ-V, n = 11), schizophrenia patients with no history of violence (SCZ-NV, n = 17), and healthy controls (HC, n = 19). Cortical thickness, area, and folding were analyzed vertex-wise across the cortical mantle (FreeSurfer). SCZ-V had significantly increased cortical folding in the visual and orbitofrontal cortex, and reduced cortical thickness within the precentral-, parietal-, temporal-, and fusiform cortex compared to SCZ-NV, as well as widespread regional thinning and increased folding compared to HC. There were no group differences in cortical area. A major limitation is the small subject sample. If replicated, the results from this pilot study suggest cortical abnormalities in areas involved in sensory processing, emotion recognition, and reward to be of importance to the neurobiology of violence in schizophrenia.

Antipsychotic treatment and basal ganglia volumes: Exploring the role of receptor occupancy, dosage and remission status

Antipsychotic treatment may affect brain morphology, and enlargement of the basal ganglia (BG) is a replicated finding. Here we investigated associations between antipsychotic treatment and BG volumes in patients with psychotic and bipolar disorders. We hypothesized that current treatment and, among those medicated, higher dosage, estimated D2R occupancy and being in remission would predict larger BG volumes. Structural covariance analysis was performed to examine if correlations between BG volumes and cortical thickness differed by treatment status. 224 patients treated with antipsychotics; 26 previously treated, 29 never treated and 301 healthy controls (HC) were included from the TOP study cohort (NORMENT, Norway). T1-weighted MR images were processed using FreeSurfer. D2R occupancy was estimated based on serum concentration measurements for patients receiving stable monotherapy. Statistical analyses were adjusted for age, gender and estimated intracranial volume (ICV). We found larger right (p < 0.003) and left putamen (p < 0.02) and right globus pallidus (GP) (p < 0.03) in currently medicated patients compared to HC. Bilateral regional cortical thinning was also observed in currently and previously medicated patients compared to HC. In medicated patients, higher chlorpromazine equivalent dose (CPZ) was associated with larger left GP (p < 0.04). There was no association with estimated D2R occupancy (n = 47) or remission status. Lower positive correlation between left putamen volume and cortical thickness of the left lateral occipital cortex was found in medicated patients compared to HC. We replicated the BG enlargement in medicated patients, but found no association with estimated D2R occupancy. Further studies are needed to clarify the underlying mechanisms.

Optimization of preprocessing strategies in Positron Emission Tomography (PET) neuroimaging: A [11C]DASB PET study

Positron Emission Tomography (PET) is an important neuroimaging tool to quantify the distribution of specific molecules in the brain. The quantification is based on a series of individually designed data preprocessing steps (pipeline) and an optimal preprocessing strategy is per definition associated with less noise and improved statistical power, potentially allowing for more valid neurobiological interpretations. In spite of this, it is currently unclear how to design the best preprocessing pipeline and to what extent the choice of each preprocessing step in the pipeline minimizes subject-specific errors. To evaluate the impact of various preprocessing strategies, we systematically examined 384 different pipeline strategies in data from 30 healthy participants scanned twice with the serotonin transporter (5-HTT) radioligand [¹¹C]DASB. Five commonly used preprocessing steps with two to four options were investigated: (1) motion correction (MC) (2) co-registration (3) delineation of volumes of interest (VOI's) (4) partial volume correction (PVC), and (5) kinetic modeling. To quantitatively compare and evaluate the impact of various preprocessing strategies, we used the performance metrics: test-retest bias, within- and between-subject variability, the intraclass-correlation coefficient, and global signal-to-noise ratio. We also performed a power analysis to estimate the required sample size to detect either a 5% or 10% difference in 5-HTT binding as a function of preprocessing pipeline. The results showed a complex downstream dependency between the various preprocessing steps on the performance metrics. The choice of MC had the most profound effect on 5-HTT binding, prior to the effects caused by PVC and kinetic modeling, and the effects differed across VOI's. Notably, we observed a negative bias in 5-HTT binding across test and retest in 98% of pipelines, ranging from 0 to 6% depending on the pipeline. Optimization of the performance metrics revealed a trade-off in within- and between-subject variability at the group-level with opposite effects (i.e. minimization of within-subject variability increased between-subject variability and vice versa). The sample size required to detect a given effect size was also compromised by the preprocessing strategy, resulting in up to 80% increases in sample size needed to detect a 5% difference in 5-HTT binding. This is the first study to systematically investigate and demonstrate the effect of choosing different preprocessing strategies on the outcome of dynamic PET studies. We provide a framework to show how optimal and maximally powered neuroimaging results can be obtained by choosing appropriate preprocessing strategies and we provide recommendations depending on the study design. In addition, the results contribute to a better understanding of methodological uncertainty and variability in preprocessing decisions for future group- and/or longitudinal PET studies.

Cortical structures and their clinical correlates in antipsychotic-naïve schizophrenia patients before and after 6 weeks of dopamine D2/3 receptor antagonist treatment

BACKGROUND

Schizophrenia has been associated with changes in both cortical thickness and surface area, but antipsychotic exposure, illness progression and substance use may confound observations. In antipsychotic-naïve schizophrenia patients, we investigated cortical thickness and surface area as well as mean curvature before and after monotherapy with amisulpride, a relatively selective dopamine D2/3 receptor antagonist.

METHODS

Fifty-six patients and 59 matched healthy controls (HCs) underwent T1-weighted 3T magnetic resonance imaging. Forty-one patients and 51 HCs were re-scanned. FreeSurfer-processed baseline, follow-up values and symmetrized percentage changes (SPC) in cortical structures were analysed using univariate analysis of variance. Clinical measures comprised psychopathology ratings, assessment of functioning and tests of premorbid and current intelligence. We applied false discovery rate correction to account for multiple comparisons.

RESULTS

At baseline, groups did not differ in cortical thickness or surface area; however, curvature in the left hemisphere was higher in patients (p = 0.015). In both patients and HCs, higher curvature was associated with lower premorbid (p = 0.009) and current intelligence (p 0.43). Cortical thickness SPC was negatively associated with symptom improvement (p = 0.002).

CONCLUSIONS

Schizophrenia appears associated with subtle, yet clinically relevant aberrations in cortical structures. Mean curvature holds promise as a sensitive supplement to cortical thickness and surface area to detect complex structural brain abnormalities.

Parcellation of the Human Cerebral Cortex Based on Molecular Targets in the Serotonin System Quantified by Positron Emission Tomography In vivo

Parcellation of distinct areas in the cerebral cortex has a long history in neuroscience and is of great value for the study of brain function, specialization, and alterations in neuropsychiatric disorders. Analysis of cytoarchitectonical features has revealed their close association with molecular profiles based on protein density. This provides a rationale for the use of in vivo molecular imaging data for parcellation of the cortex with the advantage of whole-brain coverage. In the current work, parcellation was based on expression of key players of the serotonin neurotransmitter system. Positron emission tomography was carried out for the quantification of serotonin 1A (5-HT1A, n = 30) and 5-HT2A receptors (n = 22), the serotonin-degrading enzyme monoamine oxidase A (MAO-A, n = 32) and the serotonin transporter (5-HTT, n = 24) in healthy participants. Cortical protein distribution maps were obtained using surface-based quantification. Based on k-means clustering, silhouette criterion and bootstrapping, five distinct clusters were identified as the optimal solution. The defined clusters proved of high explanatory value for the effects of psychotropic drugs acting on the serotonin system, such as antidepressants and psychedelics. Therefore, the proposed method constitutes a sensible approach towards integration of multimodal imaging data for research and development in neuropharmacology and psychiatry.

Abnormalities in gray matter microstructure in young adults with 22q11.2 deletion syndrome

BACKGROUND

22q11.2 Deletion Syndrome (22q11DS) is a genetic, neurodevelopmental disorder characterized by a chromosomal deletion and a distinct cognitive profile. Although abnormalities in the macrostructure of the cortex have been identified in individuals with 22q11DS, it is not known if there are additional microstructural changes in gray matter regions in this syndrome, and/or if such microstructural changes are associated with cognitive functioning.

METHODS

This study employed a novel diffusion MRI measure, the Heterogeneity of Fractional Anisotropy (HFA), to examine variability in the microstructural organization of the cortex in healthy young adults (N = 30) and those with 22q11DS (N = 56). Diffusion MRI, structural MRI, clinical and cognitive data were acquired.

RESULTS

Compared to controls, individuals with 22q11DS evinced increased HFA in cortical association (p = .003, d = 0.86) and paralimbic (p < .0001, d = 1.2) brain areas, whereas no significant differences were found between the two groups in primary cortical brain areas. Additionally, increased HFA of the right paralimbic area was associated with poorer performance on tests of response inhibition, i.e., the Stroop Test (rho = -0.37 p = .005) and the Gordon Diagnostic System Vigilance Commission (rho = -0.41 p = .002) in the 22q11DS group. No significant correlations were found between HFA and cognitive abilities in the healthy control group.

CONCLUSIONS

These findings suggest that cortical microstructural disorganization may be a neural correlate of response inhibition in individuals with 22q11DS. Given that the migration pattern of neural crest cells is disrupted at the time of early brain development in 22q11DS, we hypothesize that these neural alterations may be neurodevelopmental in origin, and reflect cortical dysfunction associated with cognitive deficits.

T1-MPRAGE and T2-FLAIR segmentation of cortical and subcortical brain regions-an MRI evaluation study

PURPOSE

Development of a warp-based automated brain segmentation approach of 3D fluid-attenuated inversion recovery (FLAIR) images and comparison to 3D T1-based segmentation.

METHODS

3D FLAIR and 3D T1-weighted sequences of 30 healthy subjects (mean age 29.9 ± 8.3 years, 8 female) were acquired on the same 3T MR scanner. Warp-based segmentation was applied for volumetry of total gray matter (GM), white matter (WM), and 116 atlas regions. Segmentation results of both sequences were compared using Pearson correlation (r).

RESULTS

Correlation of GM segmentation results based on FLAIR and T1 was overall good for cortical structures (mean r across all cortical structures = 0.76). Comparatively weaker results were found in the occipital lobe (r = 0.77), central region (mean r = 0.58), basal ganglia (mean r = 0.59), thalamus (r = 0.30), and cerebellum (r = 0.73). FLAIR segmentation underestimated volume of the central region compared to T1, but showed a better anatomic concordance with the occipital lobe on visual review and subcortical structures, when also compared to manual segmentation. Visual analysis of FLAIR-based WM segmentation revealed frequent misclassification of regions of high signal intensity as GM.

CONCLUSION

Warp-based FLAIR segmentation yields comparable results to T1 segmentation for most cortical GM structures and may provide anatomically more congruent segmentation of subcortical GM structures. Selected cortical regions, especially the central region and total WM, seem to be underestimated on FLAIR segmentation.

Volumetric gray matter measures of amygdala and accumbens in childhood overweight/obesity

Objectives

Neuroimaging data suggest that pediatric overweight and obesity are associated with morphological alterations in gray matter (GM) brain structures, but previous studies using mainly voxel-based morphometry (VBM) showed inconsistent results. Here, we aimed to examine the relationship between youth obesity and the volume of predefined reward system structures using magnetic resonance (MR) volumetry. We also aimed to complement volumetry with VBM-style analysis.

Methods

Fifty-one Caucasian young subjects (32 females; mean age: 13.8±1.9, range: 10.2–16.5 years) were included. Subjects were selected from a subsample of the I.Family study examined in the Hungarian center. A T₁-weighted 1 mm³ isotropic resolution image was acquired. Age- and sex-standardized body mass index (zBMI) was assessed at the day of MRI and ~1.89 years (mean±SD: 689±188 days) before the examination. Obesity related GM alterations were investigated using MR volumetry in five predefined brain structures presumed to play crucial roles in body weight regulation (hippocampus, amygdala, accumbens, caudate, putamen), as well as whole-brain and regional VBM.

Results

The volumes of accumbens and amygdala showed significant positive correlations with zBMI, while their GM densities were inversely related to zBMI. Voxel-based GM mass also showed significant negative correlation with zBMI when investigated in the predefined amygdala region, but this relationship was mediated by GM density.

Conclusions

Overweight/obesity related morphometric brain differences already seem to be present in children/adolescents. Our work highlights the disparity between volume and VBM-derived measures and that GM mass (combination of volume and density) is not informative in the context of obesity related volumetric changes. To better characterize the association between childhood obesity and GM morphometry, a combination of volumetric segmentation and VBM methods, as well as future longitudinal studies are necessary. Our results suggest that childhood obesity is associated with enlarged structural volumes, but decreased GM density in the reward system.

Using deep autoencoders to identify abnormal brain structural patterns in neuropsychiatric disorders: A large-scale multi-sample study

Machine learning is becoming an increasingly popular approach for investigating spatially distributed and subtle neuroanatomical alterations in brain-based disorders. However, some machine learning models have been criticized for requiring a large number of cases in each experimental group, and for resembling a "black box" that provides little or no insight into the nature of the data. In this article, we propose an alternative conceptual and practical approach for investigating brain-based disorders which aim to overcome these limitations. We used an artificial neural network known as "deep autoencoder" to create a normative model using structural magnetic resonance imaging data from 1,113 healthy people. We then used this model to estimate total and regional neuroanatomical deviation in individual patients with schizophrenia and autism spectrum disorder using two independent data sets (n = 263). We report that the model was able to generate different values of total neuroanatomical deviation for each disease under investigation relative to their control group (p < .005). Furthermore, the model revealed distinct patterns of neuroanatomical deviations for the two diseases, consistent with the existing neuroimaging literature. We conclude that the deep autoencoder provides a flexible and promising framework for assessing total and regional neuroanatomical deviations in neuropsychiatric populations.

[11C]SCH23390 binding to the D1-dopamine receptor in the human brain-a comparison of manual and automated methods for image analysis

Background

The D₁-dopamine receptor radioligand [¹¹C]SCH23390 has been frequently used in PET studies. In drug-naïve patients with schizophrenia, the findings have been inconsistent, with decreases, increases, and no change in the frontal cortex D₁-dopamine receptors. While these discrepancies are likely primarily due to a lack of statistical power in these studies, we speculated that an additional explanation may be the differences due to methods of image analysis between studies, affecting reliability as well as bias between groups.

Methods

Fifteen healthy subjects underwent two PET measurements with [¹¹C]SCH23390 on the same day. The binding potential (BP_ND) was compared using a 95% confidence interval following manual and automated delineation of a region of interest (ROI) as well as with and without frame-by-frame realignment.

Results

Automated target region delineation produced lower BP_ND values, while automated delineation of the reference region yielded higher BP_ND values. However, no significant differences were observed for repeatability using automated and manual delineation methods. Frame-by-frame realignment generated higher BP_ND values and improved repeatability.

Conclusions

The results suggest that the choice of ROI delineation method is not an important factor for reliability, whereas the improved results following movement correction confirm its importance in PET image analysis. Realignment is therefore especially important for measurements in patient populations such as schizophrenia or Parkinson’s disease, where motion artifacts may be more prevalent.

Electronic supplementary material

The online version of this article (10.1186/s13550-018-0416-2) contains supplementary material, which is available to authorized users.

Regional cortical thickness changes accompanying generalized tonic-clonic seizures

Objective

Generalized tonic-clonic seizures are accompanied by cardiovascular and respiratory sequelae that threaten survival. The frequency of these seizures is a major risk factor for sudden unexpected death in epilepsy (SUDEP), a leading cause of untimely death in epilepsy. The circumstances accompanying such fatal events suggest a cardiovascular or respiratory failure induced by unknown neural processes rather than an inherent cardiac or lung deficiency. Certain cortical regions, especially the insular, cingulate, and orbitofrontal cortices, are key structures that integrate sensory input and influence diencephalic and brainstem regions regulating blood pressure, cardiac rhythm, and respiration; output from those cortical regions compromised by epilepsy-associated injury may lead to cardiorespiratory dysregulation. The aim here was to assess changes in cortical integrity, reflected as cortical thickness, relative to healthy controls. Cortical alterations in areas that influence cardiorespiratory action could contribute to SUDEP mechanisms.

Methods

High-resolution T1-weighted images were collected with a 3.0-Tesla MRI scanner from 53 patients with generalized tonic-clonic seizures (Mean age ± SD: 37.1 ± 12.6 years, 22 male) at Case Western Reserve University, University College London, and the University of California at Los Angeles. Control data included 530 healthy individuals (37.1 ± 12.6 years; 220 male) from UCLA and two open access databases (OASIS and IXI). Cortical thickness group differences were assessed at all non-cerebellar brain surface locations (P < 0.05 corrected).

Results

Increased cortical thickness appeared in post-central gyri, insula, and subgenual, anterior, posterior, and isthmus cingulate cortices. Post-central gyri increases were greater in females, while males showed more extensive cingulate increases. Frontal and temporal cortex, lateral orbitofrontal, frontal pole, and lateral parietal and occipital cortices showed thinning. The extents of thickness changes were sex- and hemisphere-dependent, with only males exhibiting right-sided and posterior cingulate thickening, while females showed only left lateral orbitofrontal thinning. Regional cortical thickness showed modest correlations with seizure frequency, but not epilepsy duration.

Significance

Cortical thickening and thinning occur in patients with generalized tonic-clonic seizures, in cardiovascular and somatosensory areas, with extent of changes sex- and hemisphere-dependent. The data show injury in key autonomic and respiratory cortical areas, which may contribute to dysfunctional cardiorespiratory patterns during seizures, as well as to longer-term SUDEP risk.

Progressive symptom-associated prefrontal volume loss occurs in first-episode schizophrenia but not in affective psychosis

Although smaller gray matter volumes (GMV) in the prefrontal cortex (PFC) in schizophrenia and bipolar disorder have been reported cross-sectionally, there are, to our knowledge, no reports of longitudinal comparisons using manually drawn, gyrally based ROI, and their associations with symptoms. The object of this study was to determine whether first-episode schizophrenia (FESZ) and first-episode affective psychosis (FEAFF) patients show initial and progressive PFC GMV reduction in bilateral frontal pole, superior frontal gyrus (SFG), middle frontal gyrus (MFG), and inferior frontal gyrus (IFG) and examine their symptom associations. Twenty-one FESZ, 24 FEAFF and 23 healthy control subjects (HC) underwent 1.5T MRI with follow-up imaging on the same scanner ~ 1.5 years later. Groups were strikingly different in progressive GMV loss. FESZ showed significant progressive GMV loss in the left SFG, bilateral MFG, and bilateral IFG. In addition, left MFG and/or IFG GMV loss was associated with worsening of withdrawal-retardation and total BPRS symptoms scores. In contrast, FEAFF showed no significant difference in GMV compared with HC, either cross-sectionally or longitudinally. Of note, FreeSurfer run on the same images showed no significant changes longitudinally.

Diminution of context association memory structure in subjects with subjective cognitive decline

Alzheimer's disease (AD) progresses insidiously from the preclinical stage to dementia. While people with subjective cognitive decline (SCD) have normal cognitive performance, some may be in the preclinical stage of AD. Neurofibrillary tangles appear first in the transentorhinal cortex, followed by the entorhinal cortex in the clinically silent stage of AD. We expected the earliest changes in subjects with SCD to occur in medial temporal subfields other than the hippocampal proper. These selective structural changes would affect specific memory subcomponents. We used the Family Picture subtest of the Wechsler Memory Scale-III, which was modified to separately compute character, activity, and location subscores for episodic memory subcomponents. We recruited 43 subjects with SCD, 44 subjects with amnesic mild cognitive impairment, and 34 normal controls. MRI was used to assess cortical thickness, subcortical gray matter volume, and fractional anisotropy. The results demonstrated that SCD subjects showed significant cortical atrophy in their bilateral parahippocampus and perirhinal and the left entorhinal cortices but not in their hippocampal regions. SCD subjects also exhibited significantly decreased mean fractional anisotropy in their bilateral uncinate fasciculi. The diminution of cortical thickness over the mesial temporal subfields corresponded to brain areas with early tangle deposition, and early degradation of the uncinate fasciculus was in accordance with the retrogenesis hypothesis. The parahippocampus and perirhinal cortex contribute mainly to context association memory while the entorhinal cortex, along with the uncinate fasciculus, contributes to content-related contextual memory. We proposed that context association and related memory structures are vulnerable in the SCD stage.

Reliability and Utility of Manual and Automated Estimates of Total Intracranial Volume

OBJECTIVES

Total intracranial volume (TICV) is an important control variable in brain-behavior research, yet its calculation has challenges. Manual TICV (Manual) is labor intensive, and automatic methods vary in reliability. To identify an accurate automatic approach we assessed the reliability of two FreeSurfer TICV metrics (eTIV and Brainmask) relative to manual TICV. We then assessed how these metrics alter associations between left entorhinal cortex (ERC) volume and story retention.

METHODS

Forty individuals with Parkinson's disease (PD) and 40 non-PD peers completed a brain MRI and memory testing. Manual metrics were compared to FreeSurfer's Brainmask (a skull strip mask with total volume of gray, white, and most cerebrospinal fluid) and eTIV (calculated using the transformation matrix into Talairach space). Volumes were compared with two-way interclass correlations and dice similarity indices. Associations between ERC volume and Wechsler Memory Scale-Third Edition Logical Memory retention were examined with and without correction using each TICV method.

RESULTS

Brainmask volumes were larger and eTIV volumes smaller than Manual. Both automated metrics correlated highly with Manual. All TICV metrics explained additional variance in the ERC-Memory relationship, although none were significant. Brainmask explained slightly more variance than other methods.

CONCLUSIONS

Our findings suggest Brainmask is more reliable than eTIV for TICV correction in brain-behavioral research. (JINS, 2018, 24, 206-211).

Frontal dysconnectivity in 22q11.2 deletion syndrome: an atlas-based functional connectivity analysis

BACKGROUND

22q11.2 deletion syndrome (22q11DS) is a neurodevelopmental syndrome associated with deficits in cognitive and emotional processing. This syndrome represents one of the highest risk factors for the development of schizophrenia. Previous studies of functional connectivity (FC) in 22q11DS report aberrant connectivity patterns in large-scale networks that are associated with the development of psychotic symptoms.

METHODS

In this study, we performed a functional connectivity analysis using the CONN toolbox to test for differential connectivity patterns between 54 individuals with 22q11DS and 30 healthy controls, between the ages of 17-25 years old. We mapped resting-state fMRI data onto 68 atlas-based regions of interest (ROIs) generated by the Desikan-Killany atlas in FreeSurfer, resulting in 2278 ROI-to-ROI connections for which we determined total linear temporal associations between each. Within the group with 22q11DS only, we further tested the association between prodromal symptoms of psychosis and FC.

RESULTS

We observed that relative to controls, individuals with 22q11DS displayed increased FC in lobar networks involving the frontal-frontal, frontal-parietal, and frontal-occipital ROIs. In contrast, FC between ROIs in the parietal-temporal and occipital lobes was reduced in the 22q11DS group relative to healthy controls. Moreover, positive psychotic symptoms were positively associated with increased functional connections between the left precuneus and right superior frontal gyrus, as well as reduced functional connectivity between the bilateral pericalcarine. Positive symptoms were negatively associated with increased functional connectivity between the right pericalcarine and right postcentral gyrus.

CONCLUSIONS

Our results suggest that functional organization may be altered in 22q11DS, leading to disruption in connectivity between frontal and other lobar substructures, and potentially increasing risk for prodromal psychosis.

Recommendations for the Use of Automated Gray Matter Segmentation Tools: Evidence from Huntington's Disease

The selection of an appropriate segmentation tool is a challenge facing any researcher aiming to measure gray matter (GM) volume. Many tools have been compared, yet there is currently no method that can be recommended above all others; in particular, there is a lack of validation in disease cohorts. This work utilizes a clinical dataset to conduct an extensive comparison of segmentation tools. Our results confirm that all tools have advantages and disadvantages, and we present a series of considerations that may be of use when selecting a GM segmentation method, rather than a ranking of these tools. Seven segmentation tools were compared using 3 T MRI data from 20 controls, 40 premanifest Huntington's disease (HD), and 40 early HD participants. Segmented volumes underwent detailed visual quality control. Reliability and repeatability of total, cortical, and lobular GM were investigated in repeated baseline scans. The relationship between each tool was also examined. Longitudinal within-group change over 3 years was assessed via generalized least squares regression to determine sensitivity of each tool to disease effects. Visual quality control and raw volumes highlighted large variability between tools, especially in occipital and temporal regions. Most tools showed reliable performance and the volumes were generally correlated. Results for longitudinal within-group change varied between tools, especially within lobular regions. These differences highlight the need for careful selection of segmentation methods in clinical neuroimaging studies. This guide acts as a primer aimed at the novice or non-technical imaging scientist providing recommendations for the selection of cohort-appropriate GM segmentation software.

Affective Circuitry Alterations in Patients with Trigeminal Neuralgia

Trigeminal neuralgia (TN) is a severe chronic neuropathic facial pain disorder. Affect-related behavioral and structural brain changes have been noted across chronic pain disorders, but have not been well-studied in TN. We examined the potential impact of TN (37 patients: 23 with right-sided TN, 14 with left-sided TN), compared to age- and sex-matched healthy controls, on three major white matter tracts responsible for carrying affect-related signals—i.e., cingulum, fornix, and medial forebrain bundle. Diffusion magnetic resonance imaging (dMRI), deterministic multi-tensor tractography for tract modeling, and a model-driven region-of-interest approach was used. We also used volumetric gray matter analysis on key targets of these pathways (i.e., hippocampus, cingulate cortex subregions, nucleus accumbens, and ventral diencephalon). Hypotheses included: (1) successful modeling of tracts; (2) altered white matter microstructure of the cingulum and medial forebrain bundle (via changes in dMRI metrics such as fractional anisotropy, and mean, axial, and radial diffusivities) compared to controls; (3) no alterations in the control region of the fornix; (4) corresponding decreases in gray matter volumes. Results showed (1) all 325 tracts were successfully modeled, although 11 were partially complete; (2) The cingulum and medial forebrain bundle (MFB) were altered in those with TN, with dMRI metric changes in the middle (p = 0.001) and posterior cingulum (p < 0.0001), and the MFB near the ventral tegmental area (MFB-VTA) (p = 0.001). The posterior cingulum and MFB-VTA also showed unilateral differences between right- and left-sided TN patients; (3) No differences were noted at any fornix subdivision; (4) decreased volumes were noted for the hippocampus, posterior cingulate, nucleus accumbens, and ventral diencephalon. Together, these results support the notion of selectively altered affective circuits in patients with TN, which may be related to the experience of negative affect and the increased comorbidity of mood and anxiety disorders in this population.

Using machine learning and surface reconstruction to accurately differentiate different trajectories of mood and energy dysregulation in youth

Difficulty regulating positive mood and energy is a feature that cuts across different pediatric psychiatric disorders. Yet, little is known regarding the neural mechanisms underlying different developmental trajectories of positive mood and energy regulation in youth. Recent studies indicate that machine learning techniques can help elucidate the role of neuroimaging measures in classifying individual subjects by specific symptom trajectory. Cortical thickness measures were extracted in sixty-eight anatomical regions covering the entire brain in 115 participants from the Longitudinal Assessment of Manic Symptoms (LAMS) study and 31 healthy comparison youth (12.5 y/o;-Male/Female = 15/16;-IQ = 104;-Right/Left handedness = 24/5). Using a combination of trajectories analyses, surface reconstruction, and machine learning techniques, the present study aims to identify the extent to which measures of cortical thickness can accurately distinguish youth with higher (n = 18) from those with lower (n = 34) trajectories of manic-like behaviors in a large sample of LAMS youth (n = 115; 13.6 y/o; M/F = 68/47, IQ = 100.1, R/L = 108/7). Machine learning analyses revealed that widespread cortical thickening in portions of the left dorsolateral prefrontal cortex, right inferior and middle temporal gyrus, bilateral precuneus, and bilateral paracentral gyri and cortical thinning in portions of the right dorsolateral prefrontal cortex, left ventrolateral prefrontal cortex, and right parahippocampal gyrus accurately differentiate (Area Under Curve = 0.89;p = 0.03) youth with different (higher vs lower) trajectories of positive mood and energy dysregulation over a period up to 5years, as measured by the Parent General Behavior Inventory-10 Item Mania Scale. Our findings suggest that specific patterns of cortical thickness may reflect transdiagnostic neural mechanisms associated with different temporal trajectories of positive mood and energy dysregulation in youth. This approach has potential to identify patterns of neural markers of future clinical course.

Anatomical integration and rich-club connectivity in euthymic bipolar disorder

BACKGROUND

Although repeatedly associated with white matter microstructural alterations, bipolar disorder (BD) has been relatively unexplored using complex network analysis. This method combines structural and diffusion magnetic resonance imaging (MRI) to model the brain as a network and evaluate its topological properties. A group of highly interconnected high-density structures, termed the 'rich-club', represents an important network for integration of brain functioning. This study aimed to assess structural and rich-club connectivity properties in BD through graph theory analyses.

METHOD

We obtained structural and diffusion MRI scans from 42 euthymic patients with BD type I and 43 age- and gender-matched healthy volunteers. Weighted fractional anisotropy connections mapped between cortical and subcortical structures defined the neuroanatomical networks. Next, we examined between-group differences in features of graph properties and sub-networks.

RESULTS

Patients exhibited significantly reduced clustering coefficient and global efficiency, compared with controls globally and regionally in frontal and occipital regions. Additionally, patients displayed weaker sub-network connectivity in distributed regions. Rich-club analysis revealed subtly reduced density in patients, which did not withstand multiple comparison correction. However, hub identification in most participants indicated differentially affected rich-club membership in the BD group, with two hubs absent when compared with controls, namely the superior frontal gyrus and thalamus.

CONCLUSIONS

This graph theory analysis presents a thorough investigation of topological features of connectivity in euthymic BD. Abnormalities of global and local measures and network components provide further neuroanatomically specific evidence for distributed dysconnectivity as a trait feature of BD.

Test-retest reliability of brain morphology estimates

Metrics of brain morphology are increasingly being used to examine inter-individual differences, making it important to evaluate the reliability of these structural measures. Here we used two open-access datasets to assess the intersession reliability of three cortical measures (thickness, gyrification, and fractal dimensionality) and two subcortical measures (volume and fractal dimensionality). Reliability was generally good, particularly with the gyrification and fractal dimensionality measures. One dataset used a sequence previously optimized for brain morphology analyses and had particularly high reliability. Examining the reliability of morphological measures is critical before the measures can be validly used to investigate inter-individual differences.

Intrinsic Connectivity Network-Based Classification and Detection of Psychotic Symptoms in Youth With 22q11.2 Deletions

22q11.2 Deletion syndrome (22q11DS) is a genetic disorder associated with numerous phenotypic consequences and is one of the greatest known risk factors for psychosis. We investigated intrinsic-connectivity-networks (ICNs) as potential biomarkers for patient and psychosis-risk status in 2 independent cohorts, UCLA (33 22q11DS-participants, 33 demographically matched controls), and Syracuse (28 22q11DS, 28 controls). After assessing group connectivity differences, ICNs from the UCLA cohort were used to train classifiers to distinguish cases from controls, and to predict psychosis risk status within 22q11DS; classifiers were subsequently tested on the Syracuse cohort. In both cohorts we observed significant hypoconnectivity in 22q11DS relative to controls within anterior cingulate (ACC)/precuneus, executive, default mode (DMN), posterior DMN, and salience networks. Of 12 ICN-derived classifiers tested in the Syracuse replication-cohort, the ACC/precuneus, DMN, and posterior DMN classifiers accurately distinguished between 22q11DS and controls. Within 22q11DS subjects, connectivity alterations within 4 networks predicted psychosis risk status for a given individual in both cohorts: the ACC/precuneus, DMN, left executive, and salience networks. Widespread within-network-hypoconnectivity in large-scale networks implicated in higher-order cognition may be a defining characteristic of 22q11DS during adolescence and early adulthood; furthermore, loss of coherence within these networks may be a valuable biomarker for individual prediction of psychosis-risk in 22q11DS.

Associations between neurodevelopmental genes, neuroanatomy, and ultra high risk symptoms of psychosis in 22q11.2 deletion syndrome

22q11.2 deletion syndrome is a neurogenetic disorder resulting in the deletion of over 40 genes. Up to 40% of individuals with 22q11.2DS develop schizophrenia, though little is known about the underlying mechanisms. We hypothesized that allelic variation in functional polymorphisms in seven genes unique to the deleted region would affect lobar brain volumes, which would predict risk for psychosis in youth with 22q11.2DS. Participants included 56 individuals (30 males) with 22q11.2DS. Anatomic MR images were collected and processed using Freesurfer. Participants were genotyped for 10 SNPs in the COMT, DGCR8, GNB1L, PIK4CA, PRODH, RTN4R, and ZDHHC8 genes. All subjects were assessed for ultra high risk symptoms of psychosis. Allelic variation of the rs701428 SNP of RTN4R was significantly associated with volumetric differences in gray matter of the lingual gyrus and cuneus of the occipital lobe. Moreover, occipital gray matter volumes were robustly associated with ultra high risk symptoms of psychosis in the presence of the G allele of rs701428. Our results suggest that RTN4R, a relatively under-studied gene at the 22q11 locus, constitutes a susceptibility gene for psychosis in individuals with this syndrome through its alteration of the architecture of the brain. © 2017 Wiley Periodicals, Inc.

Cerebrospinal Fluid Levels of Amyloid Beta 1-43 Mirror 1-42 in Relation to Imaging Biomarkers of Alzheimer's Disease

Introduction: Amyloid beta 1-43 (Aβ43), with its additional C-terminal threonine residue, is hypothesized to play a role in early Alzheimer’s disease pathology possibly different from that of amyloid beta 1-42 (Aβ42). Cerebrospinal fluid (CSF) Aβ43 has been suggested as a potential novel biomarker for predicting conversion from mild cognitive impairment (MCI) to dementia in Alzheimer’s disease. However, the relationship between CSF Aβ43 and established imaging biomarkers of Alzheimer’s disease has never been assessed.

Materials and Methods: In this observational study, CSF Aβ43 was measured with ELISA in 89 subjects; 34 with subjective cognitive decline (SCD), 51 with MCI, and four with resolution of previous cognitive complaints. All subjects underwent structural MRI; 40 subjects on a 3T and 50 on a 1.5T scanner. Forty subjects, including 24 with SCD and 12 with MCI, underwent ¹⁸F-Flutemetamol PET. Seventy-eight subjects were assessed with ¹⁸F-fluorodeoxyglucose PET (21 SCD/7 MCI and 11 SCD/39 MCI on two different scanners). Ten subjects with SCD and 39 with MCI also underwent diffusion tensor imaging.

Results: Cerebrospinal fluid Aβ43 was both alone and together with p-tau a significant predictor of the distinction between SCD and MCI. There was a marked difference in CSF Aβ43 between subjects with ¹⁸F-Flutemetamol PET scans visually interpreted as negative (37 pg/ml, n = 27) and positive (15 pg/ml, n = 9), p < 0.001. Both CSF Aβ43 and Aβ42 were negatively correlated with standardized uptake value ratios for all analyzed regions; CSF Aβ43 average rho -0.73, Aβ42 -0.74. Both CSF Aβ peptides correlated significantly with hippocampal volume, inferior parietal and frontal cortical thickness and axial diffusivity in the corticospinal tract. There was a trend toward CSF Aβ42 being better correlated with cortical glucose metabolism. None of the studied correlations between CSF Aβ43/42 and imaging biomarkers were significantly different for the two Aβ peptides when controlling for multiple testing.

Conclusion: Cerebrospinal fluid Aβ43 appears to be strongly correlated with cerebral amyloid deposits in the same way as Aβ42, even in non-demented patients with only subjective cognitive complaints. Regarding imaging biomarkers, there is no evidence from the present study that CSF Aβ43 performs better than the classical CSF biomarker Aβ42 for distinguishing SCD and MCI.

Cortical-amygdala volumetric ratios predict onset of symptoms of psychosis in 22q11.2 deletion syndrome

Dysfunction of cortical circuitry involving prefrontal cortex, cingulate gyrus and mesial temporal lobe has been implicated in the pathophysiology of psychotic symptoms. 22q11.2 deletion syndrome (22q11DS) is a neurogenetic disorder that comports a 25-fold increased risk of developing psychosis. Morphological changes in the neuroanatomy of this syndrome may represent a biological risk factor for the development of psychosis. The present study explored ratios between cortical volumes and the amygdala. We also explored relationships between these ratios and the eventual development of psychosis in youth with 22q11DS. A group of 73 individuals with 22q11DS, 32 community controls, and 27 unaffected siblings were followed every three years, at four timepoints. We analyzed baseline ratios between 34 bilateral FreeSurfer-generated cortical volumes and amygdala, and examined whether baseline cortical ratios predicted positive symptoms of psychosis 12 years later, at the 4th timepoint. Youth with 22q11DS demonstrated significantly smaller cortical volume-to-amygdala ratios in left anterior cingulate, occipital and parietal cortices. An increased risk of developing psychotic episodes in individuals with 22q11DS was associated with a lower cortical volume- to-amygdala ratio, suggesting that cortico-limbic circuitry may play an important role in emotional modulation and may underlie the pathophysiology of positive symptoms of psychosis.