Cortical thickness alterations and systemic inflammation define long-COVID patients with cognitive impairment.

As the heterogeneity of symptoms is increasingly recognized among long-COVID patients, it appears highly relevant to study potential pathophysiological differences along the different subtypes. Preliminary evidence suggests distinct alterations in brain structure and systemic inflammatory patterns in specific groups of long-COVID patients. To this end, we analyzed differences in cortical thickness and peripheral immune signature between clinical subgroups based on 3 T-MRI scans and signature inflammatory markers in n = 120 participants comprising healthy never-infected controls (n = 30), healthy COVID-19 survivors (n = 29), and subgroups of long-COVID patients with (n = 26) and without (n = 35) cognitive impairment according to screening with Montreal Cognitive Assessment. Whole-brain comparison of cortical thickness between the 4 groups was conducted by surface-based morphometry. We identified distinct cortical areas showing a progressive increase in cortical thickness across different groups, starting from healthy individuals who had never been infected with COVID-19, followed by healthy COVID-19 survivors, long-COVID patients without cognitive deficits (MoCA ≥ 26), and finally, long-COVID patients exhibiting significant cognitive deficits (MoCA < 26). These findings highlight the continuum of cortical thickness alterations associated with COVID-19, with more pronounced changes observed in individuals experiencing cognitive impairment (p < 0.05, FWE-corrected). Affected cortical regions covered prefrontal and temporal gyri, insula, posterior cingulate, parahippocampal gyrus, and parietal areas. Additionally, we discovered a distinct immunophenotype, with elevated levels of IL-10, IFNγ, and sTREM2 in long-COVID patients, especially in the group suffering from cognitive impairment. We demonstrate lingering cortical and immunological alterations in healthy and impaired subgroups of COVID-19 survivors. This implies a complex underlying pathomechanism in long-COVID and emphasizes the necessity to investigate the whole spectrum of post-COVID biology to determine targeted treatment strategies targeting specific sub-groups.

Predicting disease-related MRI patterns of multiple sclerosis through GAN-based image editing.

INTRODUCTION: Multiple sclerosis (MS) is a complex neurodegenerative disorder that affects the brain and spinal cord. In this study, we applied a deep learning-based approach using the StyleGAN model to explore patterns related to MS and predict disease progression in magnetic resonance images (MRI). METHODS: We trained the StyleGAN model unsupervised using T1-weighted GRE MR images and diffusion-based ADC maps of MS patients and healthy controls. We then used the trained model to resample MR images from real input data and modified them by manipulations in the latent space to simulate MS progression. We analyzed the resulting simulation-related patterns mimicking disease progression by comparing the intensity profiles of the original and manipulated images and determined the brain parenchymal fraction (BPF). RESULTS: Our results show that MS progression can be simulated by manipulating MR images in the latent space, as evidenced by brain volume loss on both T1-weighted and ADC maps and increasing lesion extent on ADC maps. CONCLUSION: Overall, this study demonstrates the potential of the StyleGAN model in medical imaging to study image markers and to shed more light on the relationship between brain atrophy and MS progression through corresponding manipulations in the latent space.

Understanding the concept of a novel tool requires interaction of the dorsal and ventral streams.

The left hemisphere tool-use network consists of the dorso-dorsal, ventro-dorsal, and ventral streams, each with distinct computational abilities. In the dual-loop model, the ventral pathway through the extreme capsule is associated with conceptual understanding. We performed a learning experiment with fMRI to investigate how these streams interact when confronted with novel tools. In session one, subjects observed pictures and video sequences in real world action of known and unknown tools and were asked whether they knew the tools and whether they understood their function. In session two, video sequences of unknown tools were presented again, followed again by the question of understanding their function. Different conditions were compared to each other and effective connectivity (EC) in the tool-use network was examined. During concept acquisition of an unknown tool, EC between dorsal and ventral streams was found posterior in fusiform gyrus and anterior in inferior frontal gyrus, with a functional interaction between BA44d and BA45. When previously unknown tools were presented for a second time, EC was prominent only between dorsal stream areas. Understanding the concept of a novel tool requires an interaction of the ventral stream with the dorsal streams. Once the concept is acquired, dorsal stream areas are sufficient.

Planning adaptive treatment by longitudinal response assessment implementing MR imaging, liquid biopsy and analysis of microenvironment during neoadjuvant treatment of rectal cancer (PRIMO).

INTRODUCTION: Conducting neoadjuvant chemoradiotherapy (CRT) and additional preoperative consolidating chemotherapy (CTx), that is, total neoadjuvant therapy (TNT), improves local control and complete response (CR) rates in locally advanced rectal cancer (LARC), putting the focus on organ preservation concepts. Therefore, assessing response before surgery is crucial. Some LARC patients would either not benefit from intensification by TNT or may reach CR, making resection not mandatory. Treatment of LARC should therefore be based on patient individual risk and response to avoid overtreatment.The "PRIMO" pilot study aims to determine early response assessment to form a basis for development and validation of a noninvasive response prediction model by a subsequent prospective multicenter trial, which is highly needed for individual, response-driven therapy adaptions. METHODS: PRIMO is a prospective observational cohort study including adult patients with LARC receiving neoadjuvant CRT. At least 4 multiparametric magnetic resonance imaging (MRI) scans (diffusion-weighted imaging [DWI] and hypoxia-sensitive sequences) as well as repeated blood samples in order to analyze circulating tumor cells (CTC) and cell-free tumor DNA (ctDNA) are scheduled. Pelvic radiotherapy (RT, 50.4 Gy) will be performed in combination with a 5-fluorouracil/oxaliplatin regimen in all patients (planned: N = 50), succeeded by consolidation CTx (FOLFOX4) if feasible. Additional (immuno)histochemical markers, such as tumor-infiltrating lymphocytes (TIL) and programmed death ligand 1 (PD-L1) status will be analyzed before and after CRT. Routine resection is scheduled subsequently, nonoperative management is offered alternatively in case of clinical CR (cCR).The primary endpoint is pathological response; secondary endpoints comprise longitudinal changes in MRI as well as in CTCs and TIL. These are evaluated for early response prediction during neoadjuvant therapy, in order to develop a noninvasive response prediction model for subsequent analyses. DISCUSSION: Early response assessment is the key in differentiating "good" and "bad" responders during neoadjuvant CRT, allowing adaption of subsequent therapies (additional consolidating CTx, organ preservation). This study will contribute in this regard, by advancing MR imaging and substantiating new surrogate markers. Adaptive treatment strategies might build on these results in further studies.

Magnetic susceptibility anisotropy in normal appearing white matter in multiple sclerosis from single-orientation acquisition.

Quantitative susceptibility mapping (QSM) has been successfully applied to study changes in deep grey matter nuclei as well as in lesional tissue, but its application to white matter has been complicated by the observed orientation dependence of gradient echo signal. The anisotropic susceptibility tensor is thought to be at the origin of this orientation dependence, and magnetic susceptibility anisotropy (MSA) derived from this tensor has been proposed as a marker of the state and integrity of the myelin sheath and may therefore be of particular interest for the study of demyelinating pathologies such as multiple sclerosis (MS). Reconstruction of the susceptibility tensor, however, requires repeated measurements with multiple head orientations, rendering the approach impractical for clinical applications. In this study, we combined single-orientation QSM with fibre orientation information to assess apparent MSA in three white matter tracts, i.e., optic radiation (OR), splenium of the corpus callosum (SCC), and superior longitudinal fascicle (SLF), in two cohorts of 64 healthy controls and 89 MS patients. The apparent MSA showed a significant decrease in optic radiation in the MS cohort compared with healthy controls. It decreased in the MS cohort with increasing lesion load in OR and with disease duration in the splenium. All of this suggests demyelination in normal appearing white matter. However, the apparent MSA observed in the SLF pointed to potential systematic issues that require further exploration to realize the full potential of the presented approach. Despite the limitations of such single-orientation ROI-specific estimation, we believe that our clinically feasible approach to study degenerative changes in WM is worthy of further investigation.

Neural distinctiveness of fatigue and low sleep quality in multiple sclerosis.

BACKGROUND AND PURPOSE: Fatigue and low sleep quality in multiple sclerosis (MS) are closely related symptoms. Here, the associations between the brain's functional connectivity (FC) and fatigue and low sleep quality were investigated to determine the degree of neural distinctiveness of these symptoms. METHOD: A hundred and four patients with relapsing-remitting MS (age 38.9 ± 10.2 years, 66 females) completed the Modified Fatigue Impact Scale and the Pittsburgh Sleep Quality Index and underwent resting-state functional magnetic resonance imaging. FC was analyzed using independent-component analysis in sensorimotor, default-mode, fronto-parietal and basal-ganglia networks. Multiple linear regression models allowed us to test the association between FC and fatigue and sleep quality whilst controlling for one another as well as for demographic, disease-related and imaging variables. RESULTS: Higher fatigue correlated with lower sleep quality (r = 0.54, p < 0.0001). Higher fatigue was associated with lower FC of the precentral gyrus in the sensorimotor network, the precuneus in the posterior default-mode network and the superior frontal gyrus in the left fronto-parietal network, independently of sleep quality. Lower sleep quality was associated with lower FC of the left intraparietal sulcus in the left fronto-parietal network, independently of fatigue. Specific associations were found between fatigue and the sensorimotor network's global FC and between low sleep quality and the left fronto-parietal network's global FC. CONCLUSION: Despite the high correlation between fatigue and low sleep quality in the clinical picture, our findings clearly indicate that, on the neural level, fatigue and low sleep quality in MS are associated with decreased FC in distinct functional brain networks.

Investigation of biases in convolutional neural networks for semantic segmentation using performance sensitivity analysis.

The application of deep neural networks for segmentation in medical imaging has gained substantial interest in recent years. In many cases, this variant of machine learning has been shown to outperform other conventional segmentation approaches. However, little is known about its general applicability. Especially the robustness against image modifications (e.g., intensity variations, contrast variations, spatial alignment) has hardly been investigated. Data augmentation is often used to compensate for sensitivity to such changes, although its effectiveness has not yet been studied. Therefore, the goal of this study was to systematically investigate the sensitivity to variations in input data with respect to segmentation of medical images using deep learning. This approach was tested with two publicly available segmentation frameworks (DeepMedic and TractSeg). In the case of DeepMedic, the performance was tested using ground truth data, while in the case of TractSeg, the STAPLE technique was employed. In both cases, sensitivity analysis revealed significant dependence of the segmentation performance on input variations. The effects of different data augmentation strategies were also shown, making this type of analysis a useful tool for selecting the right parameters for augmentation. The proposed analysis should be applied to any deep learning image segmentation approach, unless the assessment of sensitivity to input variations can be directly derived from the network.

In vivo assessment of anisotropy of apparent magnetic susceptibility in white matter from a single orientation acquisition.

Multiple studies have reported a significant dependence of the effective transverse relaxation rate constant (R2*) and the phase of gradient-echo based (GRE) signal on the orientation of white matter fibres in the human brain. It has also been hypothesized that magnetic susceptibility, as obtained by single-orientation quantitative susceptibility mapping (QSM), exhibits such a dependence. In this study, we investigated this hypothesized relationship in a cohort of healthy volunteers. We show that R2* follows the predicted orientation dependence consistently across white matter regions, whereas the apparent magnetic susceptibility is related differently to fibre orientation across the brain and often in a complex non-monotonic manner. In addition, we explored the effect of fractional anisotropy measured by diffusion-weighted MRI on the strength of the orientation dependence and observed only a limited influence in many regions. However, with careful consideration of such an impact and the limitations imposed by the ill-posed nature of the dipole inversion process, it is possible to study magnetic susceptibility anisotropy in specific brain regions with a single orientation acquisition.

Interrelations between dopamine and serotonin producing sites and regions of the default mode network.

Recent functional magnetic resonance imaging (fMRI) studies showed that blood oxygenation level-dependent (BOLD) signal fluctuations in the default mode network (DMN) are functionally tightly connected to those in monoaminergic nuclei, producing dopamine (DA), and serotonin (5-HT) transmitters, in the midbrain/brainstem. We combined accelerated fMRI acquisition with spectral Granger causality and coherence analysis to investigate causal relationships between these areas. Both methods independently lead to similar results and confirm the existence of a top-down information flow in the resting-state condition, where activity in core DMN areas influences activity in the neuromodulatory centers producing DA/5-HT. We found that latencies range from milliseconds to seconds with high inter-subject variability, likely attributable to the resting condition. Our novel findings provide new insights into the functional organization of the human brain.

Can MRI quantify the volume changes of denervated facial muscles?

Could manual segmentation of magnetic resonance images be used to quantify the effects of transcutaneous electrostimulation and reinnervation of denervated facial muscle? Five patients with unilateral facial paralysis were scanned during the study while receiving a daily surface electrostimulation of the paralytic cheek region, but also after reinnervation. Their facial muscles were identified in 3D (coronal, sagittal, and axial) and segmented in magnetic resonance imaging (MRI) data for in total 28 time points over the 12 months of study. A non-significant trend of increasing muscle volume were detected after reinnervation. MRI is a valuable technique in the facial paralysis research.

Diffusion tensor imaging in borderline personality disorder showing prefrontal white matter alterations.

Borderline personality disorder (BPD) has repeatedly been linked to alterations in fronto-limbic dysfunction. In this study, we tested the hypothesis of disturbed structural connectivity in underlying fibre tracts and their relation to symptom profiles. We analysed diffusion tensor imaging (DTI) data from 18 female BPD patients and 38 female healthy controls. Group comparisons showed significant (p < .05, FDR adjusted) increase of radial diffusivity (RD) in the right frontal lobe, including the uncinate fasciculus, anterior thalamic radiation, and inferior fronto-occipital fasciculus, as well as overall apparent diffusion coefficient (ADC) increases in the anterior and posterior internal capsule. Symptom correlations, based on the BSL-95 questionnaires, within the BPD sample showed significant negative correlations of dysphoria with ADC the left and right anterior thalamic radiation, and positive correlations of fractional anisotropy with self-perception scores in the right superior corona radiata. While our findings add to the fronto-limbic dysfunction model of BPD, they provide additional evidence of links to its affective core pathology, particularly frontotemporal and fronto-thalamic systems.

Investigation of Deep-Learning-Driven Identification of Multiple Sclerosis Patients Based on Susceptibility-Weighted Images Using Relevance Analysis.

The diagnosis of multiple sclerosis (MS) is usually based on clinical symptoms and signs of damage to the central nervous system, which is assessed using magnetic resonance imaging. The correct interpretation of these data requires excellent clinical expertise and experience. Deep neural networks aim to assist clinicians in identifying MS using imaging data. However, before such networks can be integrated into clinical workflow, it is crucial to understand their classification strategy. In this study, we propose to use a convolutional neural network to identify MS patients in combination with attribution algorithms to investigate the classification decisions. The network was trained using images acquired with susceptibility-weighted imaging (SWI), which is known to be sensitive to the presence of paramagnetic iron components and is routinely applied in imaging protocols for MS patients. Different attribution algorithms were used to the trained network resulting in heatmaps visualizing the contribution of each input voxel to the classification decision. Based on the quantitative image perturbation method, we selected DeepLIFT heatmaps for further investigation. Single-subject analysis revealed veins and adjacent voxels as signs for MS, while the population-based study revealed relevant brain areas common to most subjects in a class. This pattern was found to be stable across different echo times and also for a multi-echo trained network. Intensity analysis of the relevant voxels revealed a group difference, which was found to be primarily based on the T1w magnitude images, which are part of the SWI calculation. This difference was not observed in the phase mask data.

Assessment of MR imaging during one-lung flooding in a large animal model.

OBJECTIVE: Magnetic resonance imaging (MRI) of the lung remains challenging due to the low tissue density, susceptibility artefacts, unfavourable relaxation times and motion. Previously, we demonstrated in vivo that one-lung flooding (OLF) with saline is a viable and safe approach. This study investigates the feasibility of OLF in an MRI environment and evaluates the flooding process on MR images. METHODS: OLF of the left lung was performed on five animals using a porcine model. Before, during and after OLF, standard T2w and T1w spin-echo (SE) and gradient-echo (GRE) sequences were applied at 3 T. RESULTS: The procedure was successfully performed in all animals. On T1w MRI, the flooded lung appeared homogenous and isointense with muscle tissue. On T2w images, vascular structures were highly hypointense, while the bronchi were clearly demarcated with hypointense wall and hyperintense lumen. The anatomical demarcation of the flooded lung from the surrounding organs was superior on T2w images. No outflow effects were seen, and no respiration triggering was required. DISCUSSION: OLF can be safely performed in an MR scanner with highly detailed visualization of the pulmonary structures on T2w images. The method provides new approaches to MRI-based image-guided pulmonary interventions using the presented experimental model.

Establishment and effects of allograft and synthetic bone graft substitute treatment of a critical size metaphyseal bone defect model in the sheep femur.

Assessment of bone graft material efficacy is difficult in humans, since invasive methods like staged CT scans or biopsies are ethically unjustifiable. Therefore, we developed a novel large animal model for the verification of a potential transformation of synthetic bone graft substitutes into vital bone. The model combines multiple imaging methods with corresponding histology in standardized critical sized cancellous bone defect. Cylindrical bone voids (10 ml) were created in the medial femoral condyles of both hind legs (first surgery at right hind leg, second surgery 3 months later at left hind leg) in three merino-wool sheep and either (i) left empty, filled with (ii) cancellous allograft bone or (iii) a synthetic, gentamicin eluting bone graft substitute. All samples were analysed with radiographs, MRI, µCT, DEXA and histology after sacrifice at 6 months. Unfilled defects only showed ingrowth of fibrous tissue, whereas good integration of the cancellous graft was seen in the allograft group. The bone graft substitute showed centripetal biodegradation and new trabecular bone formation in the periphery of the void as early as 3 months. µCT gave excellent insight into the structural changes within the defects, particularly progressive allograft incorporation and the bone graft substitute biodegradation process. MRI completed the picture by clearly visualizing soft tissue ingrowth into unfilled bone voids and presence of fluid collections. Histology was essential for verification of trabecular bone and osteoid formation. Conventional radiographs and DEXA could not differentiate details of the ongoing transformation process. This model appears well suited for detailed in vivo and ex vivo evaluation of bone graft substitute behaviour within large bone defects.

Analysis of intensity normalization for optimal segmentation performance of a fully convolutional neural network.

INTRODUCTION: Convolutional neural networks have begun to surpass classical statistical- and atlas based machine learning techniques in medical image segmentation in recent years, proving to be superior in performance and speed. However, a major challenge that the community faces are mismatch between variability within training and evaluation datasets and therefore a dependency on proper data pre-processing. Intensity normalization is a widely applied technique for reducing the variance of the data for which there are several methods available ranging from uniformity transformation to histogram equalization. The current study analyses the influence of intensity normalization on cerebellum segmentation performance of a convolutional neural network (CNN). METHOD: The study included three population samples with a total number of 218 datasets, all including a T1w MRI data set acquired at 3T and a ground truth segmentation delineating the cerebellum. A 12 layer deep 3D fully convolutional neural network was trained using 150 datasets from one of the population samples. Four different intensity normalization methods were separately applied to pre-process the data, and the CNN was correspondingly trained four times with respect to the different normalization techniques. A quantitative analysis of the segmentation performance, assessed via the Sørensen-Dice similarity coefficient (DSC) of all four CNNs, was performed to investigate the intensity sensitivity of the CNNs. Additionally, the optimal network performance was determined by identifying the best parameter set for intensity normalization. RESULTS: All four normalization methods led to excellent (mean DSC score=0.96) segmentation results when evaluated using known data; however, the segmentation performance differed depending on the applied intensity normalization method when testing with formerly unseen data, in which case the histogram equalization methods outperformed the unit distribution methods. A detailed, systematic analysis of intensity manipulations revealed, that the distribution of input intensities clearly affected the segmentation performance and that for each input dataset a linear intensity modification (shifting and scaling) existed leading to optimal segmentation results. This was further proven by an optimization analysis to find the optimal adjustment for an individual input evaluation sample within each normalization configuration. DISCUSSION: The findings suggest that proper preparation of the evaluation data is more crucial than the exact choice of normalization method to prepare the training data. The histogram equalization methods tested in this study were found to perform this task best, although leaving room for further improvements, as shown by the optimization analysis.

The Use of Physiological Signals in Brainstem/Midbrain fMRI.

Brainstem and midbrain nuclei are closely linked to cognitive performance and autonomic function. To advance the localization in this area, precise functional imaging is fundamental. In this study, we used a sophisticated fMRI technique as well as physiological recordings to investigate the involvement of brainstem/midbrain nuclei in cognitive control during a Stroop task. The temporal signal-to-noise ratio (tSNR) increased due to physiological noise correction (PNC) especially in regions adjacent to arteries and cerebrospinal fluid. Within the brainstem/cerebellum template an average tSNR of 68 ± 16 was achieved after the simultaneous application of a high-resolution fMRI, specialized co-registration, and PNC. The analysis of PNC data revealed an activation of the substantia nigra in the Stroop interference contrast whereas no significant results were obtained in the midbrain or brainstem when analyzing uncorrected data. Additionally, we found that pupil size indicated the level of cognitive effort. The Stroop interference effect on pupillary responses was correlated to the effect on reaction times (R 2 = 0.464, p < 0.05). When Stroop stimuli were modulated by pupillary responses, we observed a significant activation of the LC in the Stroop interference contrast. Thus, we demonstrated the beneficial effect of PNC on data quality and statistical results when analyzing neuronal responses to a cognitive task. Parametric modulation of task events with pupillary responses improved the model of LC BOLD activations in the Stroop interference contrast.

Real-Time Clustered Multiple Signal Classification (RTC-MUSIC).

Magnetoencephalography (MEG) and electroencephalography provide a high temporal resolution, which allows estimation of the detailed time courses of neuronal activity. However, in real-time analysis of these data two major challenges must be handled: the low signal-to-noise ratio (SNR) and the limited time available for computations. In this work, we present real-time clustered multiple signal classification (RTC-MUSIC) a real-time source localization algorithm, which can handle low SNRs and can reduce the computational effort. It provides correlation information together with sparse source estimation results, which can, e.g., be used to identify evoked responses with high sensitivity. RTC-MUSIC clusters the forward solution based on an anatomical brain atlas and optimizes the scanning process inherent to MUSIC approaches. We evaluated RTC-MUSIC by analyzing MEG auditory and somatosensory data. The results demonstrate that the proposed method localizes sources reliably. For the auditory experiment the most dominant correlated source pair was located bilaterally in the superior temporal gyri. The highest activation in the somatosensory experiment was found in the contra-lateral primary somatosensory cortex.

Improvement of olfactory function after sinus surgery correlates with white matter properties measured by diffusion tensor imaging.

Impaired olfaction is associated with a volume decrease in the olfactory bulb as well as in the gray matter of cortical olfactory areas. On the other hand, restitution of an impaired olfaction results in a regain of volume in these regions. Studies investigating similar changes in the cerebral white matter are virtually not existent. The aim of this prospective study therefore was to investigate cerebral white matter using magnetic resonance diffusion tensor imaging (DTI). 31 patients (54±13years) with olfactory impairment (chronic rhinosinusitis) and planned functional endoscopic sinus surgery (FESS) were included. Magnetic resonance imaging (MRI) data sets were acquired pre-operatively and 3months after surgery. Pre- and postoperative olfactory testing was performed to assess the olfactory threshold, discrimination, and identification (TDI) score. A significant postoperative TDI improvement by 9.06±8.81 points was observed. Two groups were subsequently formed - one with relevant postoperative olfactory gain (ΔTDI≥10 points, 12 patients) and one without gain (ΔTDI<10 points, 19 patients). DTI parameter showed a significant correlation with the TDI score in the left anterior cingulate cortex and the right amygdala. In the group with relevant olfactory improvement higher values of fractional anisotropy and apparent diffusion coefficient were found in the right parahippocampal area and in the white matter below the left inferior temporal sulcus. Tract-specific diffusion property analysis revealed significant group differences in the cingulate cortex in spatial relationship to the perisplenial cortex. Overall, this prospective study indicates structural changes in white matter after postoperative restoration of olfaction.

Diffusion tensor imaging of cingulum bundle and corpus callosum in schizophrenia vs. bipolar disorder.

Both schizophrenia and bipolar disorder show abnormalities of white matter, as seen in diffusion tensor imaging (DTI) analyses of major brain fibre bundles. While studies in each of the two conditions have indicated possible overlap in anatomical location, there are few direct comparisons between the disorders. Also, it is unclear whether phenotypically similar subgroups (e.g. patients with bipolar disorder and psychotic features) might share white matter pathologies or be rather similar. Using region-of-interest (ROI) analysis of white matter with diffusion tensor imaging (DTI) at 3 T, we analysed fractional anisotropy (FA), radial diffusivity (RD), and apparent diffusion coefficient (ADC) of the corpus callosum and cingulum bundle in 33 schizophrenia patients, 17 euthymic (previously psychotic) bipolar disorder patients, and 36 healthy controls. ANOVA analysis showed significant main effects of group for RD and ADC (both elevated in schizophrenia). Across the corpus callosum ROIs, there was not group effect on FA, but for RD (elevated in schizophrenia, lower in bipolar disorder) and ADC (higher in schizophrenia, intermediate in bipolar disorder). Our findings show similarities and difference (some gradual) across regions of the two major fibre tracts implicated in these disorders, which would be consistent with a neurobiological overlap of similar clinical phenotypes.