Magnetic Resonance Diffusion Tensor Imaging Research Articles

Structural and functional correlates of disability, motor and cognitive performances in multiple sclerosis: Focus on the globus pallidus

ObjectivesTo explore structural and functional alterations of external (GPe) and internal (GPi) globus pallidus in people with multiple sclerosis (pwMS) compared to healthy controls (HC) and analyze their relationship with measures of clinical disability, motor and cognitive impairment. MethodsSixty pwMS and 30 HC comparable for age and sex underwent 3.0T MRI, including conventional, diffusion tensor MRI and resting state (RS) functional MRI. Expanded Disability Status Scale (EDSS) scores were rated and timed 25-foot walk (T25FW) test, nine-hole peg test (9HPT), and paced auditory serial addition test (PASAT) were administered. Two operators segmented the GP into GPe and GPi. Volumes, T1/T2 ratio, diffusivity indices and seed-based RS functional connectivity (FC) of the GP and its components were assessed. ResultsPwMS had no atrophy or altered diffusivity measures of the GP. Compared to HC, pwMS had higher T1/T2 ratio in both GP regions, which correlated with EDSS score (r = 0.26–0.39, p = 0.01–0.05). RS FC analysis highlighted component-specific functional alterations in pwMS: the GPe had decreased RS FC with fronto-parietal cortices, whereas the GPi had decreased intra-GP RS FC and increased RS FC with the thalamus. Worse EDSS, 9HPT, T25FW and PASAT scores were associated with GP RS FC modifications (r=-0.51‒0.51, p < 0.001). ConclusionsStructural GP involvement in MS was homogeneous across its portions. Increased T1/T2 ratio values, possibly representing iron accumulation, were related to more severe disability. RS FC alterations of the GPe and GPi were consistent with their roles within the basal ganglia network and correlated with worse functional status, suggesting less efficient communication between structures.

The white matter characteristic of the genu of corpus callosum coupled with pain intensity and negative emotion scores in patients with trigeminal neuralgia: a multivariate analysis.

Trigeminal neuralgia (TN) is a chronic neuropathic pain disorder that not only causes intense pain but also affects the psychological health of patients. Since TN pain intensity and negative emotion may be grounded in our own pain experiences, they exhibit huge inter-individual differences. This study investigates the effect of inter-individual differences in pain intensity and negative emotion on brain structure in patients with TN and the possible pathophysiology mechanism underlying this disease. T1 weighted magnetic resonance imaging and diffusion tensor imaging scans were obtained in 46 patients with TN and 35 healthy controls. All patients with TN underwent pain-related and emotion-related questionnaires. Voxel-based morphometry and regional white matter diffusion property analysis were used to investigate whole brain grey and white matter quantitatively. Innovatively employing partial least squares correlation analysis to explore the relationship among pain intensity, negative emotion and brain microstructure in patients with TN. Significant difference in white matter integrity were identified in patients with TN compared to the healthy controls group; The most correlation brain region in the partial least squares correlation analysis was the genus of the corpus callosum, which was negatively associated with both pain intensity and negative emotion. The genu of corpus callosum plays an important role in the cognition of pain perception, the generation and conduction of negative emotions in patients with TN. These findings may deepen our understanding of the pathophysiology of TN.

Association of Blood Pressure With Brain White Matter Microstructural Integrity Assessed With MRI Diffusion Tensor Imaging in Healthy Young Adults.

High blood pressure (BP) in middle-aged and older adults is associated with a brain white matter (WM) microstructural abnormality. However, little evidence is available in healthy young adults. We investigated the associations between high BP and WM microstructural integrity in young adults. This study included 1015 healthy young adults (542 women, 22-37 years) from the Human Connectome Project. Brachial systolic and diastolic BP were measured using a semiautomatic or manual sphygmomanometer. Diffusion-weighted magnetic resonance imaging was acquired to obtain diffusion tensor imaging metrics of free water (FW) content, FW-corrected WM fractional anisotropy, axial diffusivity, radial diffusivity, and mean diffusivity. Using whole-brain voxel-wise linear regression models and ANCOVA, we examined associations of BP and hypertension stage with diffusion tensor imaging metrics after adjusting for age, sex, education, body mass index, smoking status, alcohol consumption history, and differences in the b value used for diffusion magnetic resonance imaging. Systolic and diastolic BP of the sample (mean±SD) were 122.8±13.0 and 76.0±9.9 mm Hg, respectively. Associations of BP with diffusion tensor imaging metrics revealed regional heterogeneity for FW-corrected fractional anisotropy. High BP and high hypertension stage were associated with higher FW and lower FW-corrected axial diffusivity, FW-corrected radial diffusivity, and FW-corrected mean diffusivity. Moreover, associations of high diastolic BP and hypertension stage with high FW were found only in men not in women. High BP in young adults is associated with altered brain WM microstructural integrity, suggesting that high BP may have damaging effects on brain WM microstructural integrity in early adulthood, particularly in men.

Characterization of anthracycline-induced cardiotoxicity by diffusion tensor magnetic resonance imaging.

Anthracyclines are highly potent anti-cancer drugs, but their clinical use is limited by severe cardiotoxic side effects. The impact of anthracycline-induced cardiotoxicity (AIC) on left ventricular (LV) microarchitecture and diffusion properties remains unknown. This study sought to characterize AIC by cardiovascular magnetic resonance diffusion tensor imaging (DTI). Mice were treated with Doxorubicin (DOX; n = 16) for induction of AIC or saline as corresponding control (n = 15). Cardiac function was assessed via echocardiography at the end of the study period. Whole hearts (n = 8 per group) were scanned ex vivo by high-resolution DTI at 7T. Results were correlated with histopathology and mass spectrometry imaging. Mice with AIC demonstrated systolic dysfunction (LVEF 52 ± 3% vs. 43 ± 6%, P < 0.001), impaired global longitudinal strain (-19.6 ± 2.0% vs. -16.6 ± 3.0%, P < 0.01), and cardiac atrophy (LV mass index [mg/mm], 4.3 ± 0.1 vs. 3.6 ± 0.2, P < 0.01). Regional sheetlet angles were significantly lower in AIC, whereas helix angle and relative helicity remained unchanged. In AIC, fractional anisotropy was increased (0.12 ± 0.01 vs. 0.14 ± 0.02, P < 0.05). DOX-treated mice displayed higher planar and less spherical anisotropy (CPlanar 0.07 ± 0.01 vs. 0.09 ± 0.01, P < 0.01; CSpherical 0.89 ± 0.01 vs. 0.87 ± 0.02, P < 0.05). CPlanar and CSpherical yielded good discriminatory power to distinguish between mice with and without AIC (c-index 0.91 and 0.84, respectively, P for both <0.05). AIC is associated with regional changes in sheetlet angle but no major abnormalities of global LV microarchitecture. The geometric shape of the diffusion tensor is altered in AIC. DTI may provide a new tool for myocardial characterization in patients with AIC, which warrants future clinical studies to evaluate its diagnostic utility.

Cognitive Impairment Is Related to Glymphatic System Dysfunction in Pediatric Multiple Sclerosis.

The aim of this study was to investigate whether, compared to pediatric healthy controls (HCs), the glymphatic system is impaired in pediatric multiple sclerosis (MS) patients according to their cognitive status, and to assess its association with clinical disability and MRI measures of brain structural damage. Sixty-five pediatric MS patients (females = 62%; median age = 15.5 [interquartile range, IQR = 14.5;17.0] years) and 23 age- and sex-matched HCs (females = 44%; median age = 14.1 [IQR = 11.8;16.2] years) underwent neurological, neuropsychological and 3.0 Tesla MRI assessment, including conventional and diffusion tensor imaging (DTI). We calculated the diffusion along the perivascular space (DTI-ALPS) index, a proxy of glymphatic function. Cognitive impairment (Co-I) was defined as impairment in at least 2 cognitive domains. No significant differences in DTI-ALPS index were found between HCs and cognitively preserved (Co-P) pediatric MS patients (estimated mean difference [EMD] = -0.002 [95% confidence interval = -0.069; 0.065], FDR-p = 0.956). Compared to HCs and Co-P patients, Co-I pediatric MS patients (n = 20) showed significantly lower DTI-ALPS index (EMD = -0.136 [95% confidence interval = -0.214; -0.058], FDR-p ≤ 0.004). In HCs, no associations were observed between DTI-ALPS index and normalized brain, cortical and thalamic volumes, and normal-appearing white matter (NAWM) fractional anisotropy (FA) and mean diffusivity (MD) (FDR-p ≥ 0.348). In pediatric MS patients, higher brain WM lesion volume (LV), higher NAWM MD, lower normalized thalamic volume, and lower NAWM FA were associated with lower DTI-ALPS index (FDR-p ≤ 0.016). Random Forest selected lower DTI-ALPS index (relative importance [RI] = 100%), higher brain WM LV (RI = 59.5%) NAWM MD (RI = 57.1%) and intelligence quotient (RI = 51.3%) as informative predictors of cognitive impairment (out-of-bag area under the curve = 0.762). Glymphatic system dysfunction occurs in pediatric MS, is associated with brain focal lesions, irreversible tissue loss accumulation and cognitive impairment. ANN NEUROL 2024;95:1080-1092.

Quantitative MRI assessment of peripheral nerves

Quantitative MRI assessment of peripheral nerves

Exploiting macro- and micro-structural brain changes for improved Parkinson’s disease classification from MRI data

Parkinson’s disease (PD) is the second most common neurodegenerative disease. Accurate PD diagnosis is crucial for effective treatment and prognosis but can be challenging, especially at early disease stages. This study aimed to develop and evaluate an explainable deep learning model for PD classification from multimodal neuroimaging data. The model was trained using one of the largest collections of T1-weighted and diffusion-tensor magnetic resonance imaging (MRI) datasets. A total of 1264 datasets from eight different studies were collected, including 611 PD patients and 653 healthy controls (HC). These datasets were pre-processed and non-linearly registered to the MNI PD25 atlas. Six imaging maps describing the macro- and micro-structural integrity of brain tissues complemented with age and sex parameters were used to train a convolutional neural network (CNN) to classify PD/HC subjects. Explainability of the model’s decision-making was achieved using SmoothGrad saliency maps, highlighting important brain regions. The CNN was trained using a 75%/10%/15% train/validation/test split stratified by diagnosis, sex, age, and study, achieving a ROC-AUC of 0.89, accuracy of 80.8%, specificity of 82.4%, and sensitivity of 79.1% on the test set. Saliency maps revealed that diffusion tensor imaging data, especially fractional anisotropy, was more important for the classification than T1-weighted data, highlighting subcortical regions such as the brainstem, thalamus, amygdala, hippocampus, and cortical areas. The proposed model, trained on a large multimodal MRI database, can classify PD patients and HC subjects with high accuracy and clinically reasonable explanations, suggesting that micro-structural brain changes play an essential role in the disease course.

Machine learning based on functional and structural connectivity in mild cognitive impairment

ObjectiveAlzheimer's disease (AD) is a chronic, degenerative neurological disorder characterized by progressive cognitive decline and mental behavioral abnormalities. Mild cognitive impairment (MCI) is regarded as a transitional stage in the progression from normal elderly individuals to patients with AD. While studies have identified abnormalities in brain connectivity in patients with MCI, including functional and structural connectivity, accurately identifying patients with MCI in clinical screening remains challenging. We hypothesized that utilizing machine learning (ML) based on both functional and structural connectivity could yield meaningful results in distinguishing between patients with MCI and normal elderly individuals, so as to provide valuable information for early diagnosis and precise evaluation of patients with MCI. MethodsFollowing clinical criteria, we recruited 32 patients with MCI for the patient group, and 32 normal elderly individuals for the control group. All subjects underwent examinations for resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI). Subsequently, significant functional and structural connectivity features were selected and combined with a support vector machine for classification of the patient and control groups. ResultsWe observed significantly different functional connectivity in the frontal lobe and putamen between the MCI group and normal controls. The results based on functional connectivity features demonstrated a classification accuracy of 71.88% and an area under the curve (AUC) value of 0.78. In terms of structural connectivity, we found that decreased fractional anisotropy in patients with MCI was significantly associated with Montreal Cognitive Assessment scores, specifically in regions such as the precuneus and cingulate gyrus. The classification results using the structural connectivity feature yielded an accuracy of 92.19% and an AUC value of 0.99. Lastly, combining functional and structural connectivity features resulted in a classification accuracy and AUC value of 93.75% and 0.99, respectively. ConclusionsIn this study, we demonstrated a high classification performance, underscoring the potential of both brain functional and structural connectivity in distinguishing patients with MCI from normal elderly individuals. Furthermore, the integration of functional connectivity and structural connectivity features indicated that utilizing rs-fMRI and DTI could enhance the accuracy and specificity of identifying patients with MCI compared with relying on a single neuroimaging technique.

Integration of diffusion tensor imaging parameters with mesh morphing for in-depth analysis of brain white matter fibre tracts.

Averaging is commonly used for data reduction/aggregation to analyse high-dimensional MRI data, but this often leads to information loss. To address this issue, we developed a novel technique that integrates diffusion tensor metrics along the whole volume of the fibre bundle using a 3D mesh-morphing technique coupled with principal component analysis for delineating case and control groups. Brain diffusion tensor MRI scans of high school rugby union players (n = 30, age 16-18) were acquired on a 3 T MRI before and after the sports season. A non-contact sport athlete cohort with matching demographics (n = 12) was also scanned. The utility of the new method in detecting differences in diffusion tensor metrics of the right corticospinal tract between contact and non-contact sport athletes was explored. The first step was to run automated tractography on each subject's native space. A template model of the right corticospinal tract was generated and morphed into each subject's native shape and space, matching individual geometry and diffusion metric distributions with minimal information loss. The common dimension of the 20 480 diffusion metrics allowed further data aggregation using principal component analysis to cluster the case and control groups as well as visualization of diffusion metric statistics (mean, ±2 SD). Our approach of analysing the whole volume of white matter tracts led to a clear delineation between the rugby and control cohort, which was not possible with the traditional averaging method. Moreover, our approach accounts for the individual subject's variations in diffusion tensor metrics to visualize group differences in quantitative MR data. This approach may benefit future prediction models based on other quantitative MRI methods.

Human transcriptome array analysis and diffusion tensor imaging in attention-deficit/hyperactivity disorder

The mRNA markers identified using microarray assay and diffusion tensor magnetic resonance imaging (DTI) were applied to elucidate the pathophysiology of attention-deficit hyperactivity disorder (ADHD). First, we obtained total RNA from leukocytes from three children with ADHD and three healthy controls for analysis with microarray assays. Subsequently, we applied real-time quantitative polymerase chain reaction (qRT‒PCR) assays to validate the differential expression of 7 genes (COX7B, CYCS, TFAM, UTP14A, ZNF280C, IFT57 and NDUFB5) between 130 ADHD patients and 70 controls, and we built an ADHD prediction model based on the ΔCt values of aforementioned seven genes (AUROC = 0.98). Finally, in a validation group (28 patients with ADHD and 27 healthy controls), mRNA expression of the above seven genes also significantly differentiated ADHD patients from controls (AUROC value = 0.91). The DTI analysis showed increased fractional anisotropy (FA) of the forceps minor, superior corona radiata, posterior corona radiata and anterior corona radiata in ADHD patients. Moreover, the FA of the right superior corona radiata tract was positively correlated with ΔCt levels of the COX7B gene and the IFT57 gene. The results shed a new light on a genetic profile of ADHD that may help in deciphering the white matter microstructural features in disease pathogenesis.

Investigation of the effects of Gamma Knife radiosurgery on optic pathways using diffusion tensor MRI within the first year after treatment.

To investigate the radiation-induced effects of Gamma Knife radiosurgery (GKRS) for sellar-parasellar tumors on optic pathways using DTI parameters within the first year after treatment. Twenty-five patients with sellar-parasellar tumors underwent MRI before and 3months after GKRS, including T1WI, DTI, T2WI. Moreover, 21 patients underwent follow-up DTI 6-8months after radiosurgery. ROIs were set on optic nerves, optic radiations, and control localizations; DTI parameters for each were calculated. Pre- and post-radiosurgery differences in DTI values were statistically compared and assessed with respect to tumor size changes. Following GKRS, DTI parameters, notably ADC, FA, and RD, showed statistically significant changes in optic nerves and anterior optic radiations. DTI changes were more significant in the group of cases with tumor shrinkage. In this group, DTI of the anterior optic radiations further deteriorated 3months post-GKRS, whereas acute changes in DTI parameters of the optic nerves resolved within 6-8months. DTI of central and posterior optic radiations did not differ significantly following radiosurgery; 6-8months after radiosurgery, visual function was stable in 14 (56%) patients and improved in 11 (44%), showing no correlation with tumor size changes or DTI parameters. White Matter (WM) injury in the optic pathways can be induced by Gamma Knife radiosurgery targeted to sellar and parasellar tumors. Following GKRS, microstructural abnormalities occurred in the optic radiations as well as the optic nerves within the first post-treatment year. Our findings could support modifications to radiosurgical treatment strategies to minimize the risk of permanent WM injury.

Hidden Markov Model based Predicting of Alzheimer’s Disease with graph cut segmentation using MR Diffusion Tensor Imaging (DTI)

Alzheimer’s disease (AD) is the predominant aetiology of dementia among the elderly population, accounting for about 60–70% of all instances of cognitive decline. Diffusion tensor imaging (DTI) is a contemporary methodology that enables the cartography of alterations in the microstructure of white matter (WM) in neurological diseases. Nevertheless, the effort of analysing substantial amounts of medical pictures poses significant challenges, prompting researchers to shift their focus towards machine learning. This approach encompasses a collection of computer algorithms that possess the ability to autonomously adjust their output to align with the desired goal. This work proposed the use of a combined approach using Hidden Markov Model (HMM) and MR-DTI, where Diffusion Tensor Imaging (DTI) is employed as a magnetic resonance imaging technique. The purpose of this method is to forecast the occurrence of AD. Furthermore, the statistical analysis demonstrated a significant correlation between microstructural WM changes with both output in the patient groups and cognitive functioning. This finding suggests that these abnormalities in WM might potentially serve as a biomarker for AD. The proposed method is named as Graphcut Hidden MorkovModel (Graph_HMM) is evaluated on ADNI database with statistical analysis and found that it achieves 99.8% of accuracy, 96.4% of sensitivity, 97.4% of specificity and 12.3% of MSE.

Diagnostic value of Video Electroencephalography combined with Magnetic Resonance Imaging-diffusion tensor imaging in epilepsy.

To explore the diagnostic value of video electroencephalography (VEEG) combined with magnetic resonance imaging diffusion tensor imaging (MRI-DTI) in epilepsy. In this retrospective observational study, clinical data of 60 patients who underwent both VEEG and MRI examinations at the Neurosurgery Department of Quzhou People's Hospital from December 2020 to March 2023 were retrospectively reviewed, and a total of 55 patients were finally included. We evaluated the diagnostic value of combining VEEG and MRI to determine seizure type, location of epileptic focus, and structural abnormalities of brain tissue, using surgical and pathological results as the gold standard. The accuracy of the combined approach for determining the seizure type was 98.18%, which was higher than the accuracy of MRI (85.45%, P<0.05) or VEEG (81.82%, P<0.05) alone. The accuracy of joint examination for lesion location was 100.00%, which was higher than those of MRI (85.45%, P<0.05) or VEEG (83.64%, P<0.05) alone. Similar abnormal brain tissue structure detection rates was found for both MRI and VEEG (P>0.05). The application of MRI-DTI combined with VEEG to diagnose patients with epilepsy allows for the identification of abnormal structural changes in brain and the location of lesions. Combining both approaches can improve the diagnostic accuracy of each technique alone and provide a reference for the formulation and adjustment of disease management plans.

A structural equation modeling approach using behavioral and neuroimaging markers in major depressive disorder

Major depressive disorder (MDD) has consistently proven to be a multifactorial and highly comorbid disease. Despite recent depression-related research demonstrating causalities between MDD-related factors and a small number of variables, including brain structural changes, a high-statistical power analysis of the various factors is yet to be conducted. We retrospectively analyzed data from 155 participants (84 healthy controls and 71 patients with MDD). We used magnetic resonance imaging and diffusion tensor imaging data, scales assessing childhood trauma, depression severity, cognitive dysfunction, impulsivity, and suicidal ideation. To simultaneously evaluate the causalities between multivariable, we implemented two types of MDD-specified structural equation models (SEM), the behavioral and neurobehavioral models. Behavioral SEM showed significant results in the MDD group: Comparative Fit Index [CFI] = 1.000, Root Mean Square Error of Approximation [RMSEA]) = 0.000), with a strong correlation in the scales for childhood trauma, depression severity, suicidal ideation, impulsivity, and cognitive dysfunction. Based on behavioral SEM, we established neurobehavioral models showing the best-fit in MDD, especially including the right cingulate cortex, central to the posterior corpus callosum, right putamen, pallidum, whole brainstem, and ventral diencephalon, including the thalamus (CFI >0.96, RMSEA <0.05). Our MDD-specific model revealed that the limbic-associated regions are strongly connected with childhood trauma rather than depression severity, and that they independently affect suicidal ideation and cognitive dysfunction. Furthermore, cognitive dysfunction could affect impulsivity.

Abstract WP323: Late-Stage Degeneration Due to Middle-Cerebral Artery Occlusion Observed in White Matter Tracts and Specific Forebrain Regions in Middle-Aged Female Rats is Attenuated by mir20a-3p Treatment: A Magnetic Resonance-Diffusion Tensor Imaging and Conventional Morphometry Study

Introduction: Our earlier studies show that the small non-coding RNA, mir20a-3p, is neuroprotective after stroke, and reduces sensory-motor impairments in the acute phase and cognitive decline in the long-term in female rats. Cognitive decline due to vascular diseases, such as stroke, is associated with volume loss in the cortex, the limbic structures and white matter tracts. In this study, we examined the volume of forebrain white matter tracts, cortical and limbic structures in animals with characterized cognitive impairment due to stroke, and the efficacy of microRNA treatment. Methodology: Middle-aged females were subjected to ischemic stroke using endothelin 1, injected adjacent to the left middle cerebral artery (MCA). Mir-20a-3p mimic (n=10) or scrambled oligo (n=8) was administered i.v. 4h, 24h and 70d post stroke. Animals were assessed periodically for cognitive performance up to 100d after stroke using both the cued fear conditioning test and the novel object recognition test (NORT). After perfusion fixation, T2 and diffusion tensor imaging (DTI) magnetic resonance imaging of the brain was performed. Thereafter, brains were processed for Weil myelin staining, followed by quantification of the corpus callosum and internal capsule. Results: Stroke resulted in impairment in both the cued fear conditioning test and the NORT, which was attenuated by mir-20a-3p treatment. Compared to sham (no-stroke) animals, volumetric analysis of DTI scans showed a significant reduction in the volume of substantia nigra (p&lt;0.02) and the entorhinal cortex (P=0.0402) in stroke +scrambled group, but not in Stroke+ Mir20 treated group. White matter tracts showed a significant reduction in the corpus callosum and internal capsule volume in the ischemic hemisphere as compared to non-ischemic hemisphere in MCAo animals treated with the scrambled oligo. In contrast, sham and MCAo+Mir-20a-3p animals displayed no hemispheric differences in volume in either tract. Conclusion: Cognitive changes measured with remote fear memory retrieval and NORT were associated with volume reductions in the substantia nigra, the entorhinal cortex, and the major forebrain white matter tracts due to stroke. These stroke-induced deficits were attenuated in Mir-20-3p treated animals.

Safety and efficacy of the endoscopic transsphenoidal transclival approach performed using direct cortical stimulation for pontine cavernous malformations.

Surgical treatment of brainstem cavernous malformations (CMs) is challenging. Surgery using the endoscopic transsphenoidal transclival approach (eTSTCA) is reported as a useful alternative for ventral brainstem CMs. However, CMs located in the ventral midline of the brainstem are rare, and only a small number of case reports on these CMs treated with the eTSTCA exist. The efficacy and safety of the eTSTCA have not yet been fully examined. A retrospective analysis was performed for 5 consecutive patients who underwent surgery via the eTSTCA for treating ventral pontine CMs. The average maximum CM diameter was 26.0 mm (18-38 mm). All patients underwent MR-diffusion tensor imaging, which confirmed that the corticospinal tract (CST) deviated posteriorly or laterally to the CM. Direct brainstem cortical stimulation was performed to localize the CST before making the cortical incision. After the excision of the CM, the cavity was filled with artificial CSF to make an aqueous surgical field (wet-field technique) for observing the tumor cavity and confirming complete hemostasis and resection. Total removal was achieved in all patients. The preoperative modified Rankin Scale score was 3 in 3 patients and 4 in 2 patients, whereas it was 1 in 2 patients and 0 in 3 patients 3 months after surgery. Postoperative CSF leakage was observed in 1 patient, and transient abducens nerve palsy was observed in 1 patient. No other intra- or postoperative complications were observed. MR-diffusion tensor imaging and direct brainstem cortical stimulation were useful to ascertain the proximity of the CST to the CM. The endoscope provides a clear view even underwater, and it was safe and effective to observe the entire CM cavity and confirm complete hemostasis without additional retraction of the brainstem parenchyma, including the CST. The eTSTCA provides a direct access point to the lesion and may be a safer alternative treatment for patients whose CST deviates laterally or posteriorly to the CM.

Ventral targeted anterior capsulotomy for treatment-resistant depression and obsessive-compulsive disorder: A treatment method with cases

BackgroundAblative surgery using bilateral anterior capsulotomy (BAC) is an option for treatment resistant depression (TRD) and obsessive-compulsive disorder (TROCD). The location and extent of the lesion within anterior limb of the internal capsule (ALIC) remains uncertain. Accumulating evidence has suggested that the lesion should be located ventrally while limiting the dorsal extent. Our center is now targeting specific fiber tracts within the lower half of the ALIC. MethodPresurgical diffusion tensor Magnetic Resonance Imaging (MRI) was used to identify individual fibre tracts within the ventral aspect of the ALIC in the last two patients who underwent BAC at our center. One patient had TRD and the other had both TROCD and TRD. Radiofrequency-induced thermal lesions were created in the identified targets with lesion volumes between 20 and 229 mm3 (average 95 mm3). FindingsBoth patients were responders with neither experiencing significant side effects including compromised executive functions. LimitationsThe generalizability of our findings is limited because the outcome is based on two subjects. ConclusionThis work suggests that BAC can be individually tailored and more limited to the ventral aspect of the ALIC and is effective and safe for TRD and TROCD. Accumulating data also suggests that to be clinically effective the length of the capsulotomy should be about 10mm. BAC’s use may increase with the growing utilization and mastery of magnetic resonance guided focused ultrasound.

A Systematic Evaluation of Machine Learning–Based Biomarkers for Major Depressive Disorder

Biological psychiatry aims to understand mental disorders in terms of altered neurobiological pathways. However, for one of the most prevalent and disabling mental disorders, major depressive disorder (MDD), no informative biomarkers have been identified. To evaluate whether machine learning (ML) can identify a multivariate biomarker for MDD. This study used data from the Marburg-Münster Affective Disorders Cohort Study, a case-control clinical neuroimaging study. Patients with acute or lifetime MDD and healthy controls aged 18 to 65 years were recruited from primary care and the general population in Münster and Marburg, Germany, from September 11, 2014, to September 26, 2018. The Münster Neuroimaging Cohort (MNC) was used as an independent partial replication sample. Data were analyzed from April 2022 to June 2023. Patients with MDD and healthy controls. Diagnostic classification accuracy was quantified on an individual level using an extensive ML-based multivariate approach across a comprehensive range of neuroimaging modalities, including structural and functional magnetic resonance imaging and diffusion tensor imaging as well as a polygenic risk score for depression. Of 1801 included participants, 1162 (64.5%) were female, and the mean (SD) age was 36.1 (13.1) years. There were a total of 856 patients with MDD (47.5%) and 945 healthy controls (52.5%). The MNC replication sample included 1198 individuals (362 with MDD [30.1%] and 836 healthy controls [69.9%]). Training and testing a total of 4 million ML models, mean (SD) accuracies for diagnostic classification ranged between 48.1% (3.6%) and 62.0% (4.8%). Integrating neuroimaging modalities and stratifying individuals based on age, sex, treatment, or remission status does not enhance model performance. Findings were replicated within study sites and also observed in structural magnetic resonance imaging within MNC. Under simulated conditions of perfect reliability, performance did not significantly improve. Analyzing model errors suggests that symptom severity could be a potential focus for identifying MDD subgroups. Despite the improved predictive capability of multivariate compared with univariate neuroimaging markers, no informative individual-level MDD biomarker-even under extensive ML optimization in a large sample of diagnosed patients-could be identified.

The role of the brainstem in sleep disturbances and chronic pain of Gulf War and Iraq/Afghanistan veterans.

Gulf War Illness is a type of chronic multisymptom illness, that affects about 30% of veterans deployed to the 1990-91 Persian Gulf War. Veterans deployed to Iraq/Afghanistan after 2000 are reported to have a similar prevalence of chronic multisymptom illness. More than 30 years after the Persian Gulf War, Gulf War Illness still has an unexplained symptom complex, unknown etiology and lacks definitive diagnostic criteria and effective treatments. Our recent studies have found that substantially smaller brainstem volumes and lower fiber integrity are associated with increased sleep difficulty and pain intensity in 1990-91 Persian Gulf War veterans. This study was conducted to investigate whether veterans deployed to Iraq/Afghanistan present similar brainstem damage, and whether such brainstem structural differences are associated with major symptoms as in Gulf War Illness. Here, we used structural magnetic resonance imaging and diffusion tensor imaging to measure the volumes of subcortices, brainstem subregions and white matter integrity of brainstem fiber tracts in 188 veterans including 98 Persian Gulf War veterans and 90 Iraq/Afghanistan veterans. We found that compared to healthy controls, veterans of both campaigns presented with substantially smaller volumes in brainstem subregions, accompanied by greater periaqueductal gray matter volumes. We also found that all veterans had reduced integrity in the brainstem-spinal cord tracts and the brainstem-subcortical tracts. In veterans deployed during the 1990-91 Persian Gulf War, we found that brainstem structural deficits significantly correlated with increased sleep difficulties and pain intensities, but in veterans deployed to Iraq/Afghanistan, no such effect was observed. These structural differences in the brainstem neurons and tracts may reflect autonomic dysregulation corresponding to the symptom constellation, which is characteristic of Gulf War Illness. Understanding these neuroimaging and neuropathological relationships in Gulf War and Iraq/Afghanistan veterans may improve clinical management and treatment strategies for modern war related chronic multisymptom illness.

Kiosk 5R-TA-02 - Characterization of Anthracycline-induced Cardiotoxicity by 7T Diffusion Tensor Magnetic Resonance Imaging

Kiosk 5R-TA-02 - Characterization of Anthracycline-induced Cardiotoxicity by 7T Diffusion Tensor Magnetic Resonance Imaging