Voxel-based morphometry versus region of interest: a comparison of two methods for analyzing gray matter differences in schizophrenia

How psychostimulant treatment changes the brain morphometry in adults with ADHD: sMRI Comparison study to medication-naïve adults with ADHD

ADHD is increasingly recognised as a cause of functional impairment in adults. Treatment guidelines recommend stimulants as the first-line treatment for adult ADHD, but concerns exist about long-term efficacy and potential tolerance. We investigated the long-term effect of stimulant therapy on brain morphometry in individuals with ADHD. We obtained structural MRI data from the UCLA Consortium for Neuropsychiatric Phenomics for 26 adults with ADHD. We compared grey matter volume, cortical thickness, sulcal depth, gyrification index, and fractal dimension between individuals with ADHD who had received psychostimulant treatment and medication naïve. For clinical assessment, we also compared Barratt's impulsivity score, Dickman impulsivity inventory II, and Eysenck's impulsivity inventory and investigated how brain morphometry were associated with these scores The treated group exhibited significantly higher values in surface-based metrics (FWE-corrected p-value < 0.05), including gyrification index, sulcal depth, and fractal dimension. There was an increased gyrification observed in the right Rolandic operculum, left supplementary motor area, left superior temporal gyrus, right fusiform gyrus, and left cuneus. Increased sulcal depth was detected in the inferior and superior orbitofrontal regions, while increased fractal dimension was also evident in the left superior orbitofrontal gyrus. Treated group venturesomeness scores positively correlated with the grey matter volume of the right anterior cingulate gyrus and negatively with the right superior occipital gyrus. Our results suggest a limited treatment effect on ADHD scores and grey matter volume in adults. Despite significant surface-based metrics, these changes were not accompanied by improvements in the clinical scores.

Volumetric alterations in auditory and visual subcortical nuclei following perinatal deafness in felines

In response to sensory deprivation, the brain adapts to efficiently navigate a modified perceptual environment through a process referred to as compensatory crossmodal plasticity, allowing the remaining senses to repurpose deprived regions and networks. A mechanism that has been proposed to contribute to this plasticity involves adaptations within subcortical nuclei that trigger cascading effects throughout the brain. The current study uses 7T MRI to investigate the effect of perinatal deafness on the volumes of subcortical structures in felines, focusing on key sensory nuclei within the brainstem and thalamus. Using both ROI-based and morphometric approaches, the regional macrostructure of four auditory and two visual nuclei were studied, as well as the corresponding volumetric asymmetries within and across groups. In the auditory pathway, significant bilateral volumetric reductions were revealed within the lower-level structures (cochlear nucleus, superior olivary complex, and inferior colliculus), alongside a shrinkage of solely the left medial geniculate body. Within the visual pathway, a significant bilateral volumetric reduction was found in the lateral geniculate nucleus, with the superior colliculus largely unaffected. These regional alterations, along with an extensive loss of volume throughout the brainstem of deprived cats, were attributed to disuse-driven atrophy corresponding to evolved functional demands reflective of a modified perceptual environment. Furthermore, the left-right volumetric symmetries of the control subcortex were preserved following deafness. Overall, the current study reinforces the notion that subcortical structures likely contribute to compensatory crossmodal plasticity prior to cortical processing, and that these deafness-induced adaptations appear to be influenced by both the level of the affected structure within its respective sensory processing hierarchy and the specifics of its afferent profile.

Structural changes in early-stage Parkinson's disease with resting tremor at node, edge and network level

BACKGROUND

Resting tremor in Parkinson's disease (PD) is associated with the activity in the basal ganglia and cerebello-thalamo-cortical circuits/network. However, most insights stem from functional MRI research, and structural studies, which can provide basis for and constrain functional activity, remains limited.

METHODS

We investigated the structural change in PD patients with resting tremor (PD-WR) from a network perspective. 42 early-stage PD-WR, 27 PD patients without resting tremor (PD-NR), and 56 healthy controls (HC) were included.

RESULTS

PD-WR showed lower cortical thickness in several motor-related lobules. Compared to HC, significant atrophy was found in right lobule VIIA (t = -3.076, p = 0.016, Cohen's d = 0.627), left lobule VI (t = -3.323, p = 0.007, Cohen's d = 0.678), and right lobule VI (t = -3.052, p = 0.017, Cohen's d = 0.623) in PD-WR. Compared to PD-NR, left lobule V also had a significant reduction (t = -2.958, p = 0.023, d = -0.657). PD-WR had higher fractional anisotropy in cerebello-cortical connection compared to HC (t = 3.209, p = 0.009, d = 0.926), with reduced radial (t = -2.561, p = 0.046, d = 0.739) and mean (t = 2.614, p = 0.046, d = 0.871) diffusivity compared to PD-NR. At the network level, better hierarchy (rho = 0.598, p = 0.004), small-worldness (rho = 0.621, p = 0.003), and increased nodal involvement of the thalamus (rho = 0.718, p = 0.031) and motor cortex (rho = 0.660, p = 0.055) were positively correlated with tremor amplitude.

CONCLUSION

Our study supports the alternation of the cerebello-thalamo-cortical circuit in PD-WR. However, further research with other forms of PD, a wide range of disease stage and larger sample size is needed.

Neuroanatomical correlates and predictors of psychotic symptoms in Alzheimer's disease: A systematic review and meta-analysis

BACKGROUND

Psychotic symptoms (hallucinations and delusions) are a type of neuropsychiatric symptom found during Alzheimer's Disease (AD).

OBJECTIVE

This systematic review aims to comprehensively capture, analyse, and evaluate the body of evidence that has investigated associations between brain regions/networks and psychotic symptoms in AD.

METHODS

The protocol, created according to the PRISMA guidelines, was pre-registered on OSF (https://osf.io/tg8xp/). Searches were performed using PubMed, Web of Science and PsycInfo. A partial coordinate-based meta-analysis (CBMA) was performed based on data availability.

RESULTS

Eighty-two papers were selected: delusions were found to be associated mainly with right fronto-temporal brain regions and the insula; hallucinations mainly with fronto-occipital areas; both were frequently associated with the anterior cingulate cortex. The CBMA, performed on the findings of fourteen papers on delusions, identified a cluster in the frontal lobe, one in the putamen, and a smaller one in the insula.

CONCLUSIONS

The available evidence highlights that key brain regions, predominantly in the right frontal lobe, the anterior cingulate cortex, and temporo-occipital areas, appear to underpin the different manifestations of psychotic symptoms in AD and MCI. The fronto-temporal areas identified in relation to delusions may underpin a failure to assimilate correct information and consider alternative possibilities (which might generate and maintain the delusional belief), and dysfunction within the salience network (anterior cingulate cortex and insula) may suggest a contribution for how internal and external stimuli are identified; the fronto-occipital areas linked to hallucinations may indicate diminished sensory processing and non-optimal predictive processing, that together contribute to misinterpretation of stimuli and misperceptions; the fronto-temporal and occipital areas, as well as the anterior cingulate cortex were linked to the psychotic cluster.

Structural alterations in a rumination-related network in patients with major depressive disorder

Rumination is a common symptom in major depressive disorder (MDD). Previous work has connected individual differences in rumination to structural properties in various brain regions. Some of these, such as the dorsolateral prefrontal cortex (dlPFC), have also been highlighted as being altered in MDD, suggesting a connection between structural changes and ruminative symptoms. Although informative, such localised relations have limitations in the context of a network view of the brain. To further investigate rumination-related structural changes in depression, and to situate these within potential functional networks, we acquired T1-weighted structural MRI data from patients with MDD (n = 32) and controls (n = 69). Rumination was measured with the Rumination Response Scale. Surface-based, whole-brain analysis of cortical grey-matter identified group differences in the dlPFC that were, however, not related to rumination. Instead, rumination was correlated with grey-matter properties in the right precuneus. Using normative functional connectivity analysis on an independent sample (n = 100), we show these two regions to be interconnected. Further developing a network-based perspective, it was shown that the rumination-related precuneus region is connected with networks associated with processes such as executive function, autobiographical memory, and visual perception. Notably, these processes have been connected to rumination. These results suggest that rumination in depression may be linked to focal structural changes. The effects of these focal changes on rumination may then be connected to their influence on distributed functional networks.

Gray matter volume of the feline cerebral cortex and structural plasticity following perinatal deafness

In response to sensory deprivation, the brain adapts according to contemporary demands to efficiently navigate a modified perceptual environment. This reorganization may result in improved processing of the remaining senses-a phenomenon referred to as compensatory crossmodal plasticity. One approach to explore this neuroplasticity is to consider the macrostructural changes in neural tissue that mirror this functional optimization. The current study is the first of its kind to measure MRI-derived gray matter (GM) volumes of control felines (n=30), while additionally identifying volumetric differences in response to perinatal deafness (30 ototoxically-deafened cats). To accomplish this purpose, regional and morphometric methods were performed in parallel. The regional analysis evaluated volumetric alterations of global GM, as well as the volumes of 146 regions of interest (ROIs) and 12 functional subgroupings of these ROIs. Results revealed whole-brain GM preservation; however, somatosensory and visual cortices exhibited an overall increase in volume. On a smaller scale, this analysis uncovered two auditory ROIs (second auditory cortex, A2, and ventral auditory field, VAF) that decreased in volume alongside two visual regions (anteromedial lateral suprasylvian area, AMLS and splenial visual area, SVA) that increased-all localized within the right hemisphere. Comparatively, the findings of tensor-based morphometry (TBM) generally aligned with those of the ROI-based method, as this voxel-wise approach demonstrated clusters of expansion coincident with visual- and somatosensory-related loci; although, it failed to detect any GM reductions following deafness. As distinct differences were identified in each analysis, the current study highlights the importance of employing multiple methods when exploring MRI volumetry. Overall, this study proposes that volumetric alterations within sensory loci allude to a redistribution of cortical space arising from modified perceptual demands following auditory deprivation.

Disentangling the role of gray matter volume and concentration in autism spectrum disorder: A meta-analytic investigation of 25 years of voxel-based morphometry research

Despite over two decades of neuroimaging research, a unanimous definition of the pattern of structural variation associated with autism spectrum disorder (ASD) has yet to be found. One potential impeding issue could be the sometimes ambiguous use of measurements of variations in gray matter volume (GMV) or gray matter concentration (GMC). In fact, while both can be calculated using voxel-based morphometry analysis, these may reflect different underlying pathological mechanisms. We conducted a coordinate-based meta-analysis, keeping apart GMV and GMC studies of subjects with ASD. Results showed distinct and non-overlapping patterns for the two measures. GMV decreases were evident in the cerebellum, while GMC decreases were mainly found in the temporal and frontal regions. GMV increases were found in the parietal, temporal, and frontal brain regions, while GMC increases were observed in the anterior cingulate cortex and middle frontal gyrus. Age-stratified analyses suggested that such variations are dynamic across the ASD lifespan. The present findings emphasize the importance of considering GMV and GMC as distinct yet synergistic indices in autism research.

A radiomics nomogram based on multiparametric MRI for diagnosing focal cortical dysplasia and initially identifying laterality

BACKGROUND

Focal cortical dysplasia (FCD) is the most common epileptogenic developmental malformation. The diagnosis of FCD is challenging. We generated a radiomics nomogram based on multiparametric magnetic resonance imaging (MRI) to diagnose FCD and identify laterality early.

METHODS

Forty-three patients treated between July 2017 and May 2022 with histopathologically confirmed FCD were retrospectively enrolled. The contralateral unaffected hemispheres were included as the control group. Therefore, 86 ROIs were finally included. Using January 2021 as the time cutoff, those admitted after January 2021 were included in the hold-out set (n = 20). The remaining patients were separated randomly (8:2 ratio) into training (n = 55) and validation (n = 11) sets. All preoperative and postoperative MR images, including T1-weighted (T1w), T2-weighted (T2w), fluid-attenuated inversion recovery (FLAIR), and combined (T1w + T2w + FLAIR) images, were included. The least absolute shrinkage and selection operator (LASSO) was used to select features. Multivariable logistic regression analysis was used to develop the diagnosis model. The performance of the radiomic nomogram was evaluated with an area under the curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), calibration and clinical utility.

RESULTS

The model-based radiomics features that were selected from combined sequences (T1w + T2w + FLAIR) had the highest performances in all models and showed better diagnostic performance than inexperienced radiologists in the training (AUCs: 0.847 VS. 0.664, p = 0.008), validation (AUC: 0.857 VS. 0.521, p = 0.155), and hold-out sets (AUCs: 0.828 VS. 0.571, p = 0.080). The positive values of NRI (0.402, 0.607, 0.424) and IDI (0.158, 0.264, 0.264) in the three sets indicated that the diagnostic performance of Model-Combined improved significantly. The radiomics nomogram fit well in calibration curves (p > 0.05), and decision curve analysis further confirmed the clinical usefulness of the nomogram. Additionally, the contrast (the radiomics feature) of the FCD lesions not only played a crucial role in the classifier but also had a significant correlation (r = -0.319, p < 0.05) with the duration of FCD.

CONCLUSION

The radiomics nomogram generated by logistic regression model-based multiparametric MRI represents an important advancement in FCD diagnosis and treatment.

Unraveling the pathophysiology of schizophrenia: insights from structural magnetic resonance imaging studies

Background

Schizophrenia affects about 1% of the global population. In addition to the complex etiology, linking this illness to genetic, environmental, and neurobiological factors, the dynamic experiences associated with this disease, such as experiences of delusions, hallucinations, disorganized thinking, and abnormal behaviors, limit neurological consensuses regarding mechanisms underlying this disease.

Methods

In this study, we recruited 72 patients with schizophrenia and 74 healthy individuals matched by age and sex to investigate the structural brain changes that may serve as prognostic biomarkers, indicating evidence of neural dysfunction underlying schizophrenia and subsequent cognitive and behavioral deficits. We used voxel-based morphometry (VBM) to determine these changes in the three tissue structures: the gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). For both image processing and statistical analysis, we used statistical parametric mapping (SPM).

Results

Our results show that patients with schizophrenia exhibited a significant volume reduction in both GM and WM. In particular, GM volume reductions were more evident in the frontal, temporal, limbic, and parietal lobe, similarly the WM volume reductions were predominantly in the frontal, temporal, and limbic lobe. In addition, patients with schizophrenia demonstrated a significant increase in the CSF volume in the left third and lateral ventricle regions.

Conclusion

This VBM study supports existing research showing that schizophrenia is associated with alterations in brain structure, including gray and white matter, and cerebrospinal fluid volume. These findings provide insights into the neurobiology of schizophrenia and may inform the development of more effective diagnostic and therapeutic approaches.

Apathy is associated with striatal atrophy and cognitive impairment in cerebral small vessel disease

BACKGROUND

Apathy has been considered a common neuropsychiatric symptom and an important contributor to cognitive impairment in cerebral small vessel disease (SVD). However, the mechanism leading to apathy in SVD and the process whereby apathy promotes cognitive impairments remain largely unknown. We aimed to explore the relationship between apathy, cognition, and structural changes of deep grey matter (DGM) in SVD patients.

METHODS

Participants were screened for SVD, completed assessments of apathy cognition, underwent magnetic resonance imaging (MRI) scanning, and then stratified into apathy and non-apathy groups. We used region of interest (ROI)-based, voxel-based volume, and vertex-based shape analyses to compare DGM structures between study groups. Using linear regression analysis, we examined the association between apathy, structural changes, and cognition, followed by a mediation analysis of these factors.

RESULTS

A total of sixty-four SVD participants were included, with thirty in the apathy group and thirty-four in the non-apathy group. Intergroup comparison showed significantly lower volumes in bilateral caudate, right putamen, and pallidum and smaller vertex-based shapes in the right caudate and pallidum in participants with apathy compared to those without apathy. Apathy was associated with the striatal atrophy (i.e., lower volumes and smaller shape) and independently contributed to cognitive impairments in SVD. However, the above structural differences did not mediate the association between apathy and cognitive impairments.

CONCLUSION

These results highlight the important role of striatal atrophy in apathy in SVD and call for additional studies to explore the relationship between apathy, cognition, and DGM.

Cortical connectomic mediations on gamma band synchronization in schizophrenia

Aberrant gamma frequency neural oscillations in schizophrenia have been well demonstrated using auditory steady-state responses (ASSR). However, the neural circuits underlying 40 Hz ASSR deficits in schizophrenia remain poorly understood. Sixty-six patients with schizophrenia spectrum disorders and 85 age- and gender-matched healthy controls completed one electroencephalography session measuring 40 Hz ASSR and one imaging session for resting-state functional connectivity (rsFC) assessments. The associations between the normalized power of 40 Hz ASSR and rsFC were assessed via linear regression and mediation models. We found that rsFC among auditory, precentral, postcentral, and prefrontal cortices were positively associated with 40 Hz ASSR in patients and controls separately and in the combined sample. The mediation analysis further confirmed that the deficit of gamma band ASSR in schizophrenia was nearly fully mediated by three of the rsFC circuits between right superior temporal gyrus-left medial prefrontal cortex (MPFC), left MPFC-left postcentral gyrus (PoG), and left precentral gyrus-right PoG. Gamma-band ASSR deficits in schizophrenia may be associated with deficient circuitry level connectivity to support gamma frequency synchronization. Correcting gamma band deficits in schizophrenia may require corrective interventions to normalize these aberrant networks.

Advantages of Using Both Voxel- and Surface-based Morphometry in Cortical Morphology Analysis: A Review of Various Applications

Surface-based morphometry (SBM) is extremely useful for estimating the indices of cortical morphology, such as volume, thickness, area, and gyrification, whereas voxel-based morphometry (VBM) is a typical method of gray matter (GM) volumetry that includes cortex measurement. In cases where SBM is used to estimate cortical morphology, it remains controversial as to whether VBM should be used in addition to estimate GM volume. Therefore, this review has two main goals. First, we summarize the differences between the two methods regarding preprocessing, statistical analysis, and reliability. Second, we review studies that estimate cortical morphological changes using VBM and/or SBM and discuss whether using VBM in conjunction with SBM produces additional values. We found cases in which detection of morphological change in either VBM or SBM was superior, and others that showed equivalent performance between the two methods. Therefore, we concluded that using VBM and SBM together can help researchers and clinicians obtain a better understanding of normal neurobiological processes of the brain. Moreover, the use of both methods may improve the accuracy of the detection of morphological changes when comparing the data of patients and controls.

In addition, we introduce two other recent methods as future directions for estimating cortical morphological changes: a multi-modal parcellation method using structural and functional images, and a synthetic segmentation method using multi-contrast images (such as T1- and proton density-weighted images).

Bayesian inference and dynamic prediction of multivariate joint model with functional data: An application to Alzheimer's disease

Alzheimer's disease (AD) is a severe neurodegenerative disorder impairing multiple domains, for example, cognition and behavior. Assessing the risk of AD progression and initiating timely interventions at early stages are critical to improve the quality of life for AD patients. Due to the heterogeneous nature and complex mechanisms of AD, one single longitudinal outcome is insufficient to assess AD severity and disease progression. Therefore, AD studies collect multiple longitudinal outcomes, including cognitive and behavioral measurements, as well as structural brain images such as magnetic resonance imaging (MRI). How to utilize the multivariate longitudinal outcomes and MRI data to make efficient statistical inference and prediction is an open question. In this article, we propose a multivariate joint model with functional data (MJM-FD) framework that relates multiple correlated longitudinal outcomes to a survival outcome, and use the scalar-on-function regression method to include voxel-based whole-brain MRI data as functional predictors in both longitudinal and survival models. We adopt a Bayesian paradigm to make statistical inference and develop a dynamic prediction framework to predict an individual's future longitudinal outcomes and risk of a survival event. We validate the MJM-FD framework through extensive simulation studies and apply it to the motivating Alzheimer's Disease Neuroimaging Initiative (ADNI) study.

Longitudinal grey matter changes following first episode mania in bipolar I disorder: A systematic review

BACKGROUND

While widespread grey matter (GM) changes are seen in bipolar I disorder (BD-I), it is unclear how early in the illness such changes emerge. To date there has been little synthesis of findings regarding longitudinal grey matter changes early in the course of BD-I. We conducted a systematic review to examine the evolution of GM changes in BD-I patients following the first episode of mania (FEM).

METHODS

Following PRISMA guidelines, we conducted a systematic review of studies examining longitudinal changes in GM volume (GMV), cortical thickness and/or surface area in BD-I patients following FEM. We qualitatively synthesized results regarding longitudinal GM changes in BD-I patients.

RESULTS

Fifteen studies met inclusion criteria, all examining GMV changes. Longitudinal ACC volume decrease following FEM was the most replicated finding, but was only reported in 4 out of 7 studies that examined this region as part of a whole brain/region of interest analysis, with 2 of these positive studies using an overlapping patient sample. The impact of episode recurrence, medications, and other clinical factors was inconsistently examined.

LIMITATIONS

The literature regarding GM changes early in BD-I is highly inconsistent, likely due to heterogeneity in participant characteristics, imaging methodology/analysis and duration of follow up.

CONCLUSIONS

Though there was some suggestion that structural ACC changes may represent a marker for neuroprogression following FEM, results were too inconsistent to draw any conclusions. Larger longitudinal studies examining cortical thickness/surface area, and the influence of relevant clinical factors, are needed to better understand neuroprogression in early BD-I.

Subcortical grey matter changes associated with motor symptoms evaluated by the Unified Parkinson's disease Rating Scale (part III): A longitudinal study in Parkinson's disease

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Decreased grey matter volume over time suggests a subcortical alteration in PD.

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Decreased volume in the thalamus may be related to the decline in motor skills.

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Increased volume in the pallidum may contribute to motor impairment.

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Structural changes in line with the model of basal ganglia-thalamocortical circuits.

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VBM and volumetry might capture complementary aspects of structural changes in PD.

Parkinson disease (PD) is characterized by motor deficits related to structural changes in the basal ganglia-thalamocortical circuits. However, it is still unclear the exact nature of the association between grey matter alterations and motor symptoms.

Therefore, the aim of our investigation was to identify the subcortical modifications associated with motor symptoms of PD over time - adopting voxel-based morphometry (VBM) and automated volumetry methods.

We selected fifty subjects with PD from the Parkinson’s Progression Markers Initiative (PPMI) database, who performed an MRI session at two time points: at baseline (i.e. at maximum 2 years after clinical diagnosis of PD) and after 48 months. Motor symptoms were assessed using the part III of the Unified Parkinson’s Disease Rating Scale at the two time points.

Our VBM and volumetric analyses showed a general atrophy in all subcortical regions when comparing baseline with 48 months. These findings confirmed previous observations indicating a subcortical alteration over time in PD. Furthermore, our findings supported the idea that a reduced volume in the thalamus and an increased volume in pallidum may be related to the decline in motor skills. These structural modifications are in accordance with the functional model of the basal ganglia-thalamocortical circuits controlling movements. Moreover, VBM and volumetry provided partially overlapping results, suggesting that these methods might capture complementary aspects of brain degeneration in PD.

Larger bilateral amygdalar volumes are associated with affective loss experiences

Affective loss (AL) (i.e., bereavement, relationship breakup) is a stressful life event leading to a heightened risk of developing a psychiatric disorder, for example, depression and anxiety disorder. These disorders have been associated with altered subcortical brain volumes. Little is known though, how AL in healthy subjects is linked to subcortical volumes. In a study with 196 healthy young adults, we probed the association between AL across the individual entire life span, assessed via the List of Threatening Experiences Questionnaire, and magnetic resonance imaging brain gray matter volumes (a priori selected: bilateral amygdalae, hippocampi, thalami; exploratory analyses: nuclei accumbens, caudate, putamina), segmented by use of volBrain. AL was defined as death of a first-degree relative/spouse, close relative/friend, and breakup of a marriage or steady relationship. AL was associated with larger bilateral amygdalar volumes and, after taking into account the total number of ALs, with smaller right hippocampal volumes, both irrespective of sex. Exploratory analyses of striatal volumes yielded an association of AL with larger right nucleus accumbens volumes in men, and increased caudate volumes after the loss of a first-degree relative irrespective of sex. Our data suggest that AL engenders alterations in limbic structures that likely involve processes of chronic stress and amygdala- and hippocampus-dependent fear conditioning, and resemble those observed in general anxiety disorder, childhood maltreatment, and major depressive disorder. Our exploratory findings of striatal volume alterations hint at a modulation of reward processing by AL.

Structural brain abnormalities in borderline personality disorder correlate with clinical severity and predict psychotherapy response

Although previous imaging studies in borderline personality disorder (BPD) have found brain abnormalities, the results have been inconsistent. This study aimed to investigate structural brain abnormalities using voxel-based morphometry (VBM) and cortical thickness (Cth) analyses in a large sample of patients with BPD. Additionally, we aimed to determine the correlation between structural abnormalities and clinical severity and to assess its potential value at predicting psychotherapeutic response. Sixty-one individuals with BPD and 19 healthy controls underwent magnetic resonance imaging. Participants with BPD completed several self-report clinical scales, received dialectical-behavioral therapy skills training and post-therapy changes in clinical scores were also recorded. Gray matter volume (GMV) and Cth differences between groups were compared. Within the BPD group, we further characterized the structural brain correlates of clinical severity and investigated the relationship between pre-therapy structural abnormalities and therapeutic response. As potential confounders we included age, sex, educational level, and total intracranial volume (the latter only in VBM analyses). Compared to controls, the BPD group showed a reduced GMV/Cth in prefrontal areas but increased GMV in the limbic structures (amygdala and parahippocampal regions). Prefrontal abnormalities correlated with higher baseline scores on impulsivity and general BPD severity. Increased GMV in the parahippocampal area correlated with a greater emotion dysregulation. Importantly, several baseline structural abnormalities correlated with worse response to psychotherapy. Patients with BPD showed a reduced GMV in the prefrontal areas but a greater GMV in the limbic structures. Several structural abnormalities (i.e. middle and inferior prefrontal areas, anterior insula, or parahippocampal area) correlated with clinical severity and could potentially be used as imaging biological correlates biomarkers to predict psychotherapy response.

Lower Hippocampal Volume in Patients with Schizophrenia and Bipolar Disorder: A Quantitative MRI Study

Since patients with schizophrenia (SZ) and bipolar disorder (BD) share many biological features, detecting biomarkers that differentiate SZ and BD patients is crucial for optimized treatments. High-resolution magnetic resonance imaging (MRI) is suitable for detecting subtle brain structural differences in patients with psychiatric disorders. In the present study, we adopted a neuroanatomically defined and manually delineated region of interest (ROI) method to evaluate the amygdalae, hippocampi, Heschl’s gyrus (HG), and planum temporale (PT), because these regions are crucial in the development of SZ and BD. ROI volumes were measured using high resolution MRI in 31 healthy subjects (HS), 23 SZ patients, and 21 BD patients. Right hippocampal volumes differed significantly among groups (HS > BD > SZ), whereas left hippocampal volumes were lower in SZ patients than in HS and BD patients (HS = BD > SZ). Volumes of the amygdalae, HG, and PT did not differ among the three groups. For clinical correlations, there were no significant associations between ROI volumes and demographics/clinical symptoms. Our study revealed significant lower hippocampal volume in patients with SZ and BD, and we suggest that the right hippocampal volume is a potential biomarker for differentiation between SZ and BD.

Comparing VBM and ROI analyses for detection of gray matter abnormalities in patients with bipolar disorder using MRI

Background

With the increasing efforts to a better understanding of psychiatric diseases, detection of brain morphological alterations is necessary. This study compared two methods—voxel-based morphometry (VBM) and region of interest (ROI) analyses—to identify significant gray matter changes of patients with bipolar disorder type I (BP I).

Results

The VBM findings suggested gray matter reductions in the left precentral gyrus and right precuneus of the patients compared to healthy subjects (α = 0.0005, uncorrected). However, no regions reached the level of significance in ROI analysis using the three atlases, i.e., hammers, lpba40, and neuromorphometrics atlases (α = 0.0005).

Conclusion

It can be concluded that VBM analysis seems to be more sensitive to partial changes in this study. If ROI analysis is employed in studies to detect structural brain alterations between groups, it is highly recommended to use VBM analysis besides.

Inpainting as a Technique for Estimation of Missing Voxels in Brain Imaging

Issues with model fitting (i.e. suboptimal standard deviation, linewidth/full-width-at-half-maximum, and/or signal-to-noise ratio) in multi-voxel MRI spectroscopy, or chemical shift imaging (CSI) can result in the significant loss of usable voxels. A potential solution to minimize this problem is to estimate the value of unusable voxels by utilizing information from reliable voxels in the same image. We assessed an image restoration method called inpainting as a tool to restore unusable voxels, and compared it with traditional interpolation methods (nearest neighbor, trilinear interpolation and tricubic interpolation). In order to evaluate the performance across varying image contrasts and spatial resolutions, we applied the same techniques to a T1-weighted MRI brain dataset, and N-acetylaspartate (NAA) spectroscopy maps from a CSI dataset. For all image types, inpainting exhibited superior performance (lower normalized root-mean-square errors, NRMSE) compared to all other methods considered (p's < 0.001). Inpainting maintained an average NRMSE of less than 5% even with 50% missing voxels, whereas the other techniques demonstrated up to three times that value, depending on the nature of the image. For CSI maps, inpainting maintained its superiority whether the previously unusable voxels were randomly distributed, or located in regions most commonly affected by voxel loss in real-world data. Inpainting is a promising approach for recovering unusable or missing voxels in voxel-wise analyses, particularly in imaging modalities characterized by low SNR such as CSI. We hypothesize that this technique may also be applicable for datasets from other imaging modalities, such as positron emission tomography, or dynamic susceptibility contrast MRI.

Structural abnormalities in nucleus accumbens in patients with panic disorder

BACKGROUND

Although the pathogenesis of panic attacks has been well studied in patients with panic disorder (PD), the neurobiological basis of the long-term fear memories and avoidance behavior that are often observed in PD have not been well investigated. Recent animal studies have suggested that nucleus accumbens (NAcc) plays an important role in neurobiological basis of long-term fear memories and avoidance behavior.

METHODS

Thirty-eight patients with PD and 38 matched healthy control subjects (HC) participated in this study. Differences in relative volumes and shape deformations of NAcc were evaluated between groups. Correlation analyses were conducted to quantify the association between structural abnormalities in the NAcc and trait, state anxiety measured by the State-Trait Anxiety Inventory (STAI).

RESULTS

Significant volume reductions were observed in the bilateral NAcc in the patients with PD, relative to the HC. In terms of shape differences, the PD patients demonstrated significant inward deformation of the NAcc bilaterally, compared to the HC. Degree of shape deformation in the right NAcc was associated with higher scores of the STAI-Trait, and STAI-State measures in the PD patients.

LIMITATIONS

All the patients received medication such as Psychotropic drug.

CONCLUSION

Patients with PD showed reduced volumes in the NAcc, especially in lateral regions, compared with HC. Furthermore, shape deformation in the right NAcc was associated with trait anxiety and state anxiety, which has been associated with avoidance behavior.

Structural neuroimaging of the altered brain stemming from pediatric and adolescent hearing loss-Scientific and clinical challenges

There has been a spurt in structural neuroimaging studies of the effect of hearing loss on the brain. Specifically, magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) technologies provide an opportunity to quantify changes in gray and white matter structures at the macroscopic scale. To date, there have been 32 MRI and 23 DTI studies that have analyzed structural differences accruing from pre- or peri-lingual pediatric hearing loss with congenital or early onset etiology and postlingual hearing loss in pre-to-late adolescence. Additionally, there have been 15 prospective clinical structural neuroimaging studies of children and adolescents being evaluated for cochlear implants. The results of the 70 studies are summarized in two figures and three tables. Plastic changes in the brain are seen to be multifocal rather than diffuse, that is, differences are consistent across regions implicated in the hearing, speech and language networks regardless of modes of communication and amplification. Structures in that play an important role in cognition are affected to a lesser extent. A limitation of these studies is the emphasis on volumetric measures and on homogeneous groups of subjects with hearing loss. It is suggested that additional measures of morphometry and connectivity could contribute to a greater understanding of the effect of hearing loss on the brain. Then an interpretation of the observed macroscopic structural differences is given. This is followed by discussion of how structural imaging can be combined with functional imaging to provide biomarkers for longitudinal tracking of amplification. This article is categorized under: Developmental Biology > Developmental Processes in Health and Disease Translational, Genomic, and Systems Medicine > Translational Medicine Laboratory Methods and Technologies > Imaging.

The Value of Neuroimaging Techniques in the Translation and Transdiagnostic Validation of Psychiatric Diagnoses - Selective Review

Psychiatric diagnosis has long been perceived as more of an art than a science since its foundations lie within the observation, and the self-report of the patients themselves and objective diagnostic biomarkers are lacking. Furthermore, the diagnostic tools in use not only stray away from the conventional medical framework but also remain invalidated with evidence-based concepts. However, neuroscience, as a source of valid objective knowledge has initiated the process of a paradigm shift underlined by the main concept of psychiatric disorders being "brain disorders". It is also a bridge closing the explanatory gap among the different fields of medicine via the translation of the knowledge within a multidisciplinary framework. The contemporary neuroimaging methods, such as fMRI provide researchers with an entirely new set of tools to reform the current status quo by creating an opportunity to define and validate objective biomarkers that can be translated into clinical practice. Combining multiple neuroimaging techniques with the knowledge of the role of genetic factors, neurochemical imbalance and neuroinflammatory processes in the etiopathophysiology of psychiatric disorders is a step towards a comprehensive biological explanation of psychiatric disorders and a final differentiation of psychiatry as a well-founded medical science. In addition, the neuroscientific knowledge gained thus far suggests a necessity for directional change to exploring multidisciplinary concepts, such as multiple causality and dimensionality of psychiatric symptoms and disorders. A concomitant viewpoint transition of the notion of validity in psychiatry with a focus on an integrative validatory approach may facilitate the building of a collaborative bridge above the wall existing between the scientific fields analyzing the mind and those studying the brain.

Does Hippocampal Volume Predict Transition to Psychosis in a High-Risk Group? A Meta-Analysis

Schizophrenia has a prodromal phase of several years in most patients, making it possible to identify patients at clinical high risk (CHR) for developing the disorder. So far, these individuals are identified based on clinical criteria alone, and there is no reliable biomarker for predicting the transition to psychosis. It is well-established that reductions in brain volume, especially in the hippocampus, are associated with schizophrenia. Therefore, hippocampal volume may serve as a biomarker for psychosis. Several studies have already investigated hippocampal volume in CHR groups. Based on these studies, the present meta-analysis compares the baseline left and right hippocampal volume of CHR patients who developed a psychosis with that of CHR patients without such a transition. Our results show no statistically significant effect of the hippocampal volume on the transition risk for psychosis.

Associations between brain morphology and motor performance in chronic neck pain: A whole-brain surface-based morphometry approach

Changes in brain morphology are hypothesized to be an underlying process that drive the widespread pain and motor impairment in patients with chronic neck pain. However, no earlier research assessed whole-brain cortical morphology in these patients. This case-control study assesses group-differences in whole-brain morphology between female healthy controls (HC; n = 34), and female patients with chronic idiopathic neck pain (CINP; n = 37) and whiplash-associated disorders (CWAD; n = 39). Additionally, the associations between whole-brain morphology and motor performance including balance, strength, and neuromuscular control were assessed. Cortical volume, thickness, and surface area were derived from high resolution T1-weighted images. T2*-weighted images were obtained to exclude traumatic brain injury. Vertex-wise general-linear-model-analysis revealed cortical thickening in the left precuneus and increased volume in the left superior parietal gyrus of patients with CINP compared to HC, and cortical thickening of the left superior parietal gyrus compared to HC and CWAD. Patients with CWAD showed a smaller cortical volume in the right precentral and superior temporal gyrus compared to HC. ANCOVA-analysis revealed worse neuromuscular control in CWAD compared to HC and CINP, and in CINP compared to HC. Patients with CWAD showed decreased levels of strength and sway area compared to CINP and HC. Partial correlation analysis revealed significant associations between the volume of the precentral gyrus, and neuromuscular control and strength together with an association between the volume of the superior temporal gyrus and strength. Our results emphasize the role of altered gray matter alterations in women with chronic neck pain, and its association with pain and motor impairment.

Brain Morphometry Methods for Feature Extraction in Random Subspace Ensemble Neural Network Classification of First-Episode Schizophrenia

Machine learning (ML) is a growing field that provides tools for automatic pattern recognition. The neuroimaging community currently tries to take advantage of ML in order to develop an auxiliary diagnostic tool for schizophrenia diagnostics. In this letter, we present a classification framework based on features extracted from magnetic resonance imaging (MRI) data using two automatic whole-brain morphometry methods: voxel-based (VBM) and deformation-based morphometry (DBM). The framework employs a random subspace ensemble-based artificial neural network classifier—in particular, a multilayer perceptron (MLP). The framework was tested on data from first-episode schizophrenia patients and healthy controls. The experiments differed in terms of feature extraction methods, using VBM, DBM, and a combination of both morphometry methods. Thus, features of different types were available for model adaptation. As we expected, the combination of features increased the MLP classification accuracy up to 73.12%—an improvement of 5% versus MLP-based only on VBM or DBM features. To further verify the findings, other comparisons using support vector machines in place of MLPs were made within the framework. However, it cannot be concluded that any classifier was better than another.

Vasogenic cerebral edema associated with the disability in activities of daily living in patients with chronic obstructive pulmonary disease

INTRODUCTION

The aim of this study was to explore whether patients with chronic obstructive pulmonary disease (COPD) develop vasogenic cerebral edema, and whether this edema contributes to the COPD-related disability.

METHODS

Eighteen stable patients with COPD and 17 matched healthy volunteers were enrolled. Apparent diffusion coefficient (ADC) values were calculated by voxel-based analysis using DTI-Studio software based on diffusion tensor imaging. COPD-related disability was calculated using activities of daily living (ADL) scale.

RESULTS

In patients with COPD, ADC increased in the white matter fiber tracts including the bilateral anterior cingulum and posterior corpus callosum and in the white matter fibers connecting the bilateral insular cortices, sub-lobar cortices, and pars triangularis cortices and the left rectus and olfactory gyrus. However, after further controlling for cigarette smoking, the difference in ADC values in the posterior corpus callosum between groups disappeared. Patients with COPD had significantly higher scores in ADL than that in controls. Moreover, ADL scores were positively correlated with the increased regional ADC values.

CONCLUSION

Vasogenic cerebral edema occurs in patients with COPD. Cigarette smoking may be a risk factor for COPD-related vasogenic edema. Vasogenic cerebral edema may be related to the COPD-related ADL impairment.

MIDAS: Regionally linear multivariate discriminative statistical mapping

Statistical parametric maps formed via voxel-wise mass-univariate tests, such as the general linear model, are commonly used to test hypotheses about regionally specific effects in neuroimaging cross-sectional studies where each subject is represented by a single image. Despite being informative, these techniques remain limited as they ignore multivariate relationships in the data. Most importantly, the commonly employed local Gaussian smoothing, which is important for accounting for registration errors and making the data follow Gaussian distributions, is usually chosen in an ad hoc fashion. Thus, it is often suboptimal for the task of detecting group differences and correlations with non-imaging variables. Information mapping techniques, such as searchlight, which use pattern classifiers to exploit multivariate information and obtain more powerful statistical maps, have become increasingly popular in recent years. However, existing methods may lead to important interpretation errors in practice (i.e., misidentifying a cluster as informative, or failing to detect truly informative voxels), while often being computationally expensive. To address these issues, we introduce a novel efficient multivariate statistical framework for cross-sectional studies, termed MIDAS, seeking highly sensitive and specific voxel-wise brain maps, while leveraging the power of regional discriminant analysis. In MIDAS, locally linear discriminative learning is applied to estimate the pattern that best discriminates between two groups, or predicts a variable of interest. This pattern is equivalent to local filtering by an optimal kernel whose coefficients are the weights of the linear discriminant. By composing information from all neighborhoods that contain a given voxel, MIDAS produces a statistic that collectively reflects the contribution of the voxel to the regional classifiers as well as the discriminative power of the classifiers. Critically, MIDAS efficiently assesses the statistical significance of the derived statistic by analytically approximating its null distribution without the need for computationally expensive permutation tests. The proposed framework was extensively validated using simulated atrophy in structural magnetic resonance imaging (MRI) and further tested using data from a task-based functional MRI study as well as a structural MRI study of cognitive performance. The performance of the proposed framework was evaluated against standard voxel-wise general linear models and other information mapping methods. The experimental results showed that MIDAS achieves relatively higher sensitivity and specificity in detecting group differences. Together, our results demonstrate the potential of the proposed approach to efficiently map effects of interest in both structural and functional data.

Gray matter differences in the anterior cingulate and orbitofrontal cortex of young adults with Internet gaming disorder: Surface-based morphometry

Background and aims Altered risk/reward decision-making is suggested to predispose individuals with Internet gaming disorder (IGD) to pursue short-term pleasure, despite long-term negative consequences. The anterior cingulate cortex (ACC) and the orbitofrontal cortex (OFC) play important roles in risk/reward decision-making. This study investigated gray matter differences in the ACC and OFC of young adults with and without IGD using surface-based morphometry (SBM). Methods We examined 45 young male adults with IGD and 35 age-matched male controls. We performed region of interest (ROI)-based analyses for cortical thickness and gray matter volume (GMV) in the ACC and OFC. We also conducted whole-brain vertex-wise analysis of cortical thickness to complement the ROI-based analysis. Results IGD subjects had thinner cortices in the right rostral ACC, right lateral OFC, and left pars orbitalis than controls. We also found smaller GMV in the right caudal ACC and left pars orbitalis in IGD subjects. Thinner cortex of the right lateral OFC in IGD subjects correlated with higher cognitive impulsivity. Whole-brain analysis in IGD subjects revealed thinner cortex in the right supplementary motor area, left frontal eye field, superior parietal lobule, and posterior cingulate cortex. Conclusions Individuals with IGD had a thinner cortex and a smaller GMV in the ACC and OFC, which are critical areas for evaluating reward values, error processing, and adjusting behavior. In addition, in behavioral control-related brain regions, including frontoparietal areas, they also had thinner cortices. These gray matter differences may contribute to IGD pathophysiology through altered risk/reward decision-making and diminished behavioral control.

The Association Between Retinal Neuronal Layer and Brain Structure is Disrupted in Patients with Cognitive Impairment and Alzheimer's Disease

Both healthy and pathological aging due to Alzheimer's disease (AD) are associated with decreased brain grey matter volume (GMV) and disrupted white matter (WM) microstructure. Thinner macular ganglion cell-inner plexiform layer (GC-IPL) measured by spectral-domain optical coherence tomography has been reported in patients with AD and mild cognitive impairment. Emerging evidence suggested a link between thinner GC-IPL and lower GMV in subjects with no dementia using region-of-interest-based approach. However, it remains unknown whether GC-IPL thickness is associated with brain WM microstructure and how such association differed between normal and cognitively impaired subjects. Here, for subjects with no cognitive impairment (NCI), thinner GC-IPL was associated with lower WM microstructure integrity in the superior longitudinal fasciculus, inferior fronto-occipital fasciculus, corticospinal tracts, anterior thalamic radiation, and cingulum regions, while it was weakly associated with lower GMV in visual cortex and cerebellum. Nevertheless, these retina-brain associations were disrupted in the presence of cognitive impairment. Correlations between GMV and GC-IPL were lost in patients with cognitive impairment but no dementia (CIND) and AD patients. GC-IPL was related to WM microstructural disruption in similar regions with decreased significance. In contrast, lower WM microstructure integrity in the fornix showed a trend of correlation with thinner GC-IPL in both CIND and AD but not NCI. Collectively, our findings suggest the possible physiological retina-brain relationship in healthy aging, which might be disrupted by disease-induced changes in patients with cognitive impairment. Longitudinal study with larger patient sample should follow to confirm the disease mechanism behind these retina-brain relationship changes.

Imaging and Genetic Biomarkers Predicting Transition to Psychosis

The search for diagnostic and prognostic biomarkers in schizophrenia care and treatment is the focus of many within the research community. Longitudinal cohorts of patients presenting at elevated genetic and clinical risk have provided a wealth of data that has informed our understanding of the development of schizophrenia and related psychotic disorders.Imaging follow-up of high-risk cohorts has demonstrated changes in cerebral grey matter of those that eventually transition to schizophrenia that predate the onset of symptoms and evolve over the course of illness. Longitudinal follow-up studies demonstrate that observed grey matter changes can be employed to differentiate those who will transition to schizophrenia from those who will not prior to the onset of the disorder.In recent years our understanding of the genetic makeup of schizophrenia has advanced significantly. The development of modern analysis techniques offers researchers the ability to objectively quantify genetic risk; these have been successfully applied within a high-risk paradigm to assist in differentiating between high-risk individuals who will subsequently become unwell and those who will not.This chapter will discuss the application of imaging and genetic biomarkers within high-risk groups to predict future transition to schizophrenia and related psychotic disorders. We aim to provide an overview of current approaches focussing on grey matter changes that are predictive of future transition to illness, the developing field of genetic risk scores and other methods being developed to aid clinicians in diagnosis and prognosis.

MRI in the Study of Animal Models of Neurodegenerative Diseases

Magnetic Resonance Imaging (MRI) is an important tool to study various animal models of degenerative diseases. This chapter describes routine protocols of T ₁-, T ₂-, and T ₂*-weighted and diffusion-weighted MRI for rodent brain and spinal cord. These protocols can be used to measure atrophy, axonal and myelin injury and changes in white matter connectivity.

Meta-analysis of regional white matter volume in bipolar disorder with replication in an independent sample using coordinates, T-maps, and individual MRI data

Converging evidence suggests that bipolar disorder (BD) is associated with white matter (WM) abnormalities. Meta-analyses of voxel based morphometry (VBM) data is commonly performed using published coordinates, however this method is limited since it ignores non-significant data. Obtaining statistical maps from studies (T-maps) as well as raw MRI datasets increases accuracy and allows for a comprehensive analysis of clinical variables. We obtained coordinate data (7-studies), T-Maps (12-studies, including unpublished data) and raw MRI datasets (5-studies) and analysed the 24 studies using Seed-based d Mapping (SDM). A VBM analysis was conducted to verify the results in an independent sample. The meta-analysis revealed decreased WM volume in the posterior corpus callosum extending to WM in the posterior cingulate cortex. This region was significantly reduced in volume in BD patients in the independent dataset (p=0.003) but there was no association with clinical variables. We identified a robust WM volume abnormality in BD patients that may represent a trait marker of the disease and used a novel methodology to validate the findings.

Hemispheric asymmetry of the frontolimbic cortex in young adults with borderline personality disorder

OBJECTIVE

Although the frontolimbic cortex has been implicated in borderline personality disorder (BPD), information about possible asymmetries in this region in patients with BPD is limited. This study aimed to examine whether frontolimbic cortex asymmetries differ between patients with BPD and healthy individuals.

METHODS

The brains of 30 young adult patients with BPD and 32 healthy control subjects were scanned with magnetic resonance imaging (MRI). The participants completed self-report scales assessing impulsivity, affect intensity and other psychological variables. Gray matter volume, surface area, and cortical thickness in regions of interest (ROIs), namely anterior insula (AI) and anterior cingulate cortex (ACC) were determined and the data were probed for hemisphere-group interactions.

RESULTS

Relative to controls, patients with BPD had reduced cortical thickness in left ACC and less surface area and gray matter volume in left AI. Significant group-hemisphere interactions were observed for gray matter volume and surface area of AI and for cortical thickness of ACC. Post hoc analysis showed that the BPD patients had greater frontolimbic cortex asymmetry than healthy controls; furthermore, greater asymmetry of AI&ACC correlated with a higher score in attention subscale of Barratt Impulsiveness Scale.

CONCLUSION

Patients with BPD have greater frontolimbic asymmetry than healthy individuals.

Schizophrenia Shows Disrupted Links between Brain Volume and Dynamic Functional Connectivity

Studies featuring multimodal neuroimaging data fusion for understanding brain function and structure, or disease characterization, leverage the partial information available in each of the modalities to reveal data variations not exhibited through the independent analyses. Similar to other complex syndromes, the characteristic brain abnormalities in schizophrenia may be better understood with the help of the additional information conveyed by leveraging an advanced modeling method involving multiple modalities. In this study, we propose a novel framework to fuse feature spaces corresponding to functional magnetic resonance imaging (functional) and gray matter (structural) data from 151 schizophrenia patients and 163 healthy controls. In particular, the features for the functional and structural modalities include dynamic (i.e., time-varying) functional network connectivity (dFNC) maps and the intensities of the gray matter (GM) maps, respectively. The dFNC maps are estimated from group independent component analysis (ICA) network time-courses by first computing windowed functional correlations using a sliding window approach, and then estimating subject specific states from this windowed data using temporal ICA followed by spatio-temporal regression. For each subject, the functional data features are horizontally concatenated with the corresponding GM features to form a combined feature space that is subsequently decomposed through a symmetric multimodal fusion approach involving a combination of multiset canonical correlation analysis (mCCA) and joint ICA (jICA). Our novel combined analyses successfully linked changes in the two modalities and revealed significantly disrupted links between GM volumes and time-varying functional connectivity in schizophrenia. Consistent with prior research, we found significant group differences in GM comprising regions in the superior parietal lobule, precuneus, postcentral gyrus, medial/superior frontal gyrus, superior/middle temporal gyrus, insula and fusiform gyrus, and several significant aberrations in the inter-regional functional connectivity strength as well. Importantly, structural and dFNC measures have independently shown changes associated with schizophrenia, and in this work we begin the process of evaluating the links between the two, which could shed light on the illness beyond what we can learn from a single imaging modality. In future work, we plan to evaluate replication of the inferred structure-function relationships in independent partitions of larger multi-modal schizophrenia datasets.

Subcortical structural volumes in recently remitted first episode mania

BACKGROUND

Magnetic resonance imaging (MRI) studies have yielded inconsistent findings with regard to subcortical volumetric abnormalities in patients with bipolar I disorder. Duration of illness and long term medication intake could have confounded the findings.

METHOD

Volumes of nine subcortical structures were compared between 63 patients who recently remitted from their first manic episode and 77 healthy volunteers. The volumetric segmentation was performed with the automated segmentation algorithm Freesurfer version 5.1.

RESULTS

There were no significant volumetric differences between the two groups in any of the structures examined including caudate, putamen, globus pallidum, nucleus accumbens, amygdala, thalamus, cerebellum, hippocampus and lateral ventricles (q > 0.05-false discovery rate corrected).

LIMITATIONS

All patients were on psychotropic medications at the time of scanning, which might have confounded the results. Sample size may not be large enough to detect small volumetric changes.

CONCLUSIONS

Patients with bipolar I disorder do not appear to have any significant subcortical volumetric abnormalities during the early stage of the disease. Thus, early stage bipolar disorder may present an opportunity for intervention to arrest neuroprogression of the disease.

Structural Brain Abnormalities of Attention-Deficit/Hyperactivity Disorder With Oppositional Defiant Disorder

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) is associated with structural abnormalities in total gray matter, basal ganglia, and cerebellum. Findings of structural abnormalities in frontal and temporal lobes, amygdala, and insula are less consistent. Remarkably, the impact of comorbid oppositional defiant disorder (ODD) (comorbidity rates up to 60%) on these neuroanatomical differences is scarcely studied, while ODD (in combination with conduct disorder) has been associated with structural abnormalities of the frontal lobe, amygdala, and insula. The aim of this study was to investigate the effect of comorbid ODD on cerebral volume and cortical thickness in ADHD.

METHODS

Three groups, 16 ± 3.5 years of age (mean ± SD; range 7-29 years), were studied on volumetric and cortical thickness characteristics using structural magnetic resonance imaging (surface-based morphometry): ADHD+ODD (n = 67), ADHD-only (n = 243), and control subjects (n = 233). Analyses included the moderators age, gender, IQ, and scan site.

RESULTS

ADHD+ODD and ADHD-only showed volumetric reductions in total gray matter and (mainly) frontal brain areas. Stepwise volumetric reductions (ADHD+ODD < ADHD-only < control subjects) were found for mainly frontal regions, and ADHD+ODD was uniquely associated with reductions in several structures (e.g., the precuneus). In general, findings remained significant after accounting for ADHD symptom severity. There were no group differences in cortical thickness. Exploratory voxelwise analyses showed no group differences.

CONCLUSIONS

ADHD+ODD and ADHD-only were associated with volumetric reductions in brain areas crucial for attention, (working) memory, and decision-making. Volumetric reductions of frontal lobes were largest in the ADHD+ODD group, possibly underlying observed larger impairments in neurocognitive functions. Previously reported striatal abnormalities in ADHD may be caused by comorbid conduct disorder rather than ODD.

White matter microstructural abnormalities in amnestic mild cognitive impairment: A meta-analysis of whole-brain and ROI-based studies

Studies that examined white matter (WM) alterations in amnestic mild cognitive impairment (aMCI) abound. This timely meta-analysis aims to synthesize the results of these studies. Seventy-seven studies (totalN_aMCI=1844) were included. Fourteen region-of-interest-based (ROI-based) (k≥8;N_aMCI≥284 per ROI) and two activation likelihood estimation (ALE) meta-analyses (fractional anisotropy [FA]: k=15;N_aMCI=463; mean diffusivity [MD]: k=8;N_aMCI=193) were carried out. Among the many significant ROI-related findings, reliable FA and MD alterations in the fornix, uncinate fasciculus, and parahippocampal cingulum were observed in aMCI. Larger effects were observed in MD relative to FA. The ALE meta-analysis revealed a significant FA decrease among aMCI subjects in the posterior corona radiata. These results provide robust evidence of the presence of WM abnormalities in aMCI. Our findings also highlight the importance of carrying out both ROI-based and whole-brain-based research to obtain a complete picture of WM microstructural alterations associated with the condition..

Classification of Schizophrenia Based on Individual Hierarchical Brain Networks Constructed From Structural MRI Images

With structural magnetic resonance imaging (MRI) images, conventional methods for the classification of schizophrenia (SCZ) and healthy control (HC) extract cortical thickness independently at different regions of interest (ROIs) without considering the correlation between these regions. In this paper, we proposed an improved method for the classification of SCZ and HC based on individual hierarchical brain networks constructed from structural MRI images. Our method involves constructing individual hierarchical networks where each node and each edge in these networks represents a ROI and the correlation between a pair of ROIs, respectively. We demonstrate that edge features make significant improvement in performance of SCZ/HC classification, when compared with only node features. Classification performance is further investigated by combining edge features with node features via a multiple kernel learning framework. The experimental results show that our proposed method achieves an accuracy of 88.72% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.9521 for SCZ/HC classification, which demonstrate that our proposed method is efficient and promising for clinical applications for the diagnosis of SCZ via structural MRI images. Therefore, this paper provides an alternative method for extracting high-order cortical thickness features from structural MRI images for classification of neurodegenerative diseases such as SCZ.

Evolution of structural abnormalities in the rat brain following in utero exposure to maternal immune activation: A longitudinal in vivo MRI study

Genetic and environmental risk factors for psychiatric disorders are suggested to disrupt the trajectory of brain maturation during adolescence, leading to the development of psychopathology in adulthood. Rodent models are powerful tools to dissect the specific effects of such risk factors on brain maturational profiles, particularly when combined with Magnetic Resonance Imaging (MRI; clinically comparable technology). We therefore investigated the effect of maternal immune activation (MIA), an epidemiological risk factor for adult-onset psychiatric disorders, on rat brain maturation using atlas and tensor-based morphometry analysis of longitudinal in vivo MR images. Exposure to MIA resulted in decreases in the volume of several cortical regions, the hippocampus, amygdala, striatum, nucleus accumbens and unexpectedly, the lateral ventricles, relative to controls. In contrast, the volumes of the thalamus, ventral mesencephalon, brain stem and major white matter tracts were larger, relative to controls. These volumetric changes were maximal between post-natal day 50 and 100 with no differences between the groups thereafter. These data are consistent with and extend prior studies of brain structure in MIA-exposed rodents. Apart from the ventricular findings, these data have robust face validity to clinical imaging findings reported in studies of individuals at high clinical risk for a psychiatric disorder. Further work is now required to address the relationship of these MRI changes to behavioral dysfunction and to establish thier cellular correlates.

Inferring pathobiology from structural MRI in schizophrenia and bipolar disorder: Modeling head motion and neuroanatomical specificity

Despite over 400 peer-reviewed structural MRI publications documenting neuroanatomic abnormalities in bipolar disorder and schizophrenia, the confounding effects of head motion and the regional specificity of these defects are unclear. Using a large cohort of individuals scanned on the same research dedicated MRI with broadly similar protocols, we observe reduced cortical thickness indices in both illnesses, though less pronounced in bipolar disorder. While schizophrenia (n = 226) was associated with wide-spread surface area reductions, bipolar disorder (n = 227) and healthy comparison subjects (n = 370) did not differ. We replicate earlier reports that head motion (estimated from time-series data) influences surface area and cortical thickness measurements and demonstrate that motion influences a portion, but not all, of the observed between-group structural differences. Although the effect sizes for these differences were small to medium, when global indices were covaried during vertex-level analyses, between-group effects became nonsignificant. This analysis raises doubts about the regional specificity of structural brain changes, possible in contrast to functional changes, in affective and psychotic illnesses as measured with current imaging technology. Given that both schizophrenia and bipolar disorder showed cortical thickness reductions, but only schizophrenia showed surface area changes, and assuming these measures are influenced by at least partially unique sets of biological factors, then our results could indicate some degree of specificity between bipolar disorder and schizophrenia. Hum Brain Mapp 38:3757-3770, 2017. © 2017 Wiley Periodicals, Inc.

Altered microstructure rather than morphology in the corpus callosum after lower limb amputation

The corpus callosum (CC) has been implicated in the reorganization of the brain following amputation. However, it is unclear which regions of the CC are involved in this process. In this study, we explored the morphometric and microstructural changes in CC subregions in patients with unilateral lower limb amputation. Thirty-eight patients and 38 age- and gender-matched normal controls were included. The CC was divided into five regions, and the area, thickness and diffusion parameters of each region were investigated. While morphometric analysis showed no significant differences between the two groups, amputees showed significant higher values in axial diffusivity, radial diffusivity and mean diffusivity in region II of the CC, which connects the bilateral premotor and supplementary motor areas. In contrast, the mean fractional anisotropy value of the fibers generated by these cortical areas, as measured by tractography, was significantly smaller in amputees. These results demonstrate that the interhemispheric pathways contributing to motor coordination and imagery are reorganized in lower limb amputees.

Region-of-interest analyses of one-dimensional biomechanical trajectories: bridging 0D and 1D theory, augmenting statistical power

One-dimensional (1D) kinematic, force, and EMG trajectories are often analyzed using zero-dimensional (0D) metrics like local extrema. Recently whole-trajectory 1D methods have emerged in the literature as alternatives. Since 0D and 1D methods can yield qualitatively different results, the two approaches may appear to be theoretically distinct. The purposes of this paper were (a) to clarify that 0D and 1D approaches are actually just special cases of a more general region-of-interest (ROI) analysis framework, and (b) to demonstrate how ROIs can augment statistical power. We first simulated millions of smooth, random 1D datasets to validate theoretical predictions of the 0D, 1D and ROI approaches and to emphasize how ROIs provide a continuous bridge between 0D and 1D results. We then analyzed a variety of public datasets to demonstrate potential effects of ROIs on biomechanical conclusions. Results showed, first, that a priori ROI particulars can qualitatively affect the biomechanical conclusions that emerge from analyses and, second, that ROIs derived from exploratory/pilot analyses can detect smaller biomechanical effects than are detectable using full 1D methods. We recommend regarding ROIs, like data filtering particulars and Type I error rate, as parameters which can affect hypothesis testing results, and thus as sensitivity analysis tools to ensure arbitrary decisions do not influence scientific interpretations. Last, we describe open-source Python and MATLAB implementations of 1D ROI analysis for arbitrary experimental designs ranging from one-sample t tests to MANOVA.

A Voxel-Based Morphometry Study Reveals Local Brain Structural Alterations Associated with Ambient Fine Particles in Older Women

Objective: Exposure to ambient fine particulate matter (PM_2.5: PM with aerodynamic diameters < 2.5 μm) has been linked with cognitive deficits in older adults. Using fine-grained voxel-wise analyses, we examined whether PM_2.5 exposure also affects brain structure.

Methods: Brain MRI data were obtained from 1365 women (aged 71–89) in the Women's Health Initiative Memory Study and local brain volumes were estimated using RAVENS (regional analysis of volumes in normalized space). Based on geocoded residential locations and air monitoring data from the U.S. Environmental Protection Agency, we employed a spatiotemporal model to estimate long-term (3-year average) exposure to ambient PM_2.5 preceding MRI scans. Voxel-wise linear regression models were fit separately to gray matter (GM) and white matter (WM) maps to analyze associations between brain structure and PM_2.5 exposure, with adjustment for potential confounders.

Results: Increased PM_2.5 exposure was associated with smaller volumes in both cortical GM and subcortical WM areas. For GM, associations were clustered in the bilateral superior, middle, and medial frontal gyri. For WM, the largest clusters were in the frontal lobe, with smaller clusters in the temporal, parietal, and occipital lobes. No statistically significant associations were observed between PM_2.5 exposure and hippocampal volumes.

Conclusions: Long-term PM_2.5 exposures may accelerate loss of both GM and WM in older women. While our previous work linked smaller WM volumes to PM_2.5, this is the first neuroimaging study reporting associations between air pollution exposure and smaller volumes of cortical GM. Our data support the hypothesized synaptic neurotoxicity of airborne particles.

Posterior structural brain volumes differ in maltreated youth with and without chronic posttraumatic stress disorder

Magnetic resonance imaging studies of maltreated children with posttraumatic stress disorder (PTSD) suggest that maltreatment-related PTSD is associated with adverse brain development. Maltreated youth resilient to chronic PTSD were not previously investigated and may elucidate neuromechanisms of the stress diathesis that leads to resilience to chronic PTSD. In this cross-sectional study, anatomical volumetric and corpus callosum diffusion tensor imaging measures were examined using magnetic resonance imaging in maltreated youth with chronic PTSD (N = 38), without PTSD (N = 35), and nonmaltreated participants (n = 59). Groups were sociodemographically similar. Participants underwent assessments for strict inclusion/exclusion criteria and psychopathology. Maltreated youth with PTSD were psychobiologically different from maltreated youth without PTSD and nonmaltreated controls. Maltreated youth with PTSD had smaller posterior cerebral and cerebellar gray matter volumes than did maltreated youth without PTSD and nonmaltreated participants. Cerebral and cerebellar gray matter volumes inversely correlated with PTSD symptoms. Posterior corpus callosum microstructure in pediatric maltreatment-related PTSD differed compared to maltreated youth without PTSD and controls. The group differences remained significant when controlling for psychopathology, numbers of Axis I disorders, and trauma load. Alterations of these posterior brain structures may result from a shared trauma-related mechanism or an inherent vulnerability that mediates the pathway from chronic PTSD to comorbidity.

Progressive brain changes in children and adolescents with early-onset psychosis: A meta-analysis of longitudinal MRI studies

BACKGROUND

Studies on longitudinal brain volume changes in patients with early-onset psychosis (EOP) are particularly valuable for understanding the neurobiological basis of brain abnormalities associated with psychosis. However, findings have not been consistent across studies in this population. We aimed to conduct a meta-analysis on progressive brain volume changes in children and adolescents with EOP.

METHODS

A systematic literature search of magnetic resonance imaging (MRI) studies comparing longitudinal brain volume changes in children and adolescents with EOP and healthy controls was conducted. The annualized rates of relative change in brain volume by region of interest (ROI) were used as raw data for the meta-analysis. The effect of age, sex, duration of illness, and specific diagnosis on volume change was also evaluated.

RESULTS

Five original studies with 156 EOP patients (mean age at baseline MRI in the five studies ranged from 13.3 to 16.6years, 67.31% males) and 163 age- and sex-matched healthy controls, with a mean duration of follow-up of 2.46years (range 2.02-3.40), were included. Frontal gray matter (GM) was the only region in which significant differences in volume change over time were found between patients and controls (Hedges' g -0.435, 95% confidence interval (CI): -0.678 to -0.193, p<0.001). Younger age at baseline MRI was associated with greater loss of temporal GM volume over time in patients as compared with controls (p=0.005). Within patients, a diagnosis of schizophrenia was related to greater occipital GM volume loss over time (p=0.001).

CONCLUSIONS

Compared with healthy individuals, EOP patients show greater progressive frontal GM loss over the first few years after illness onset. Age at baseline MRI and diagnosis of schizophrenia appear to be significant moderators of particular specific brain volume changes.