Functional connectivity-based classification of rapid eye movement sleep behavior disorder.

BACKGROUND: Isolated rapid eye movement sleep behavior disorder (iRBD) is a clinically important parasomnia syndrome preceding α-synucleinopathies, thereby prompting us to develop methods for evaluating latent brain states in iRBD. Resting-state functional magnetic resonance imaging combined with a machine learning-based classification technology may help us achieve this purpose. METHODS: We developed a machine learning-based classifier using functional connectivity to classify 55 patients with iRBD and 97 healthy elderly controls (HC). Selecting 55 HCs randomly from the HC dataset 100 times, we conducted a classification of iRBD and HC for each sampling, using functional connectivity. Random forest ranked the importance of functional connectivity, which was subsequently used for classification with logistic regression and a support vector machine. We also conducted correlation analysis of the selected functional connectivity with subclinical variations in motor and non-motor functions in the iRBDs. RESULTS: Mean classification performance using logistic regression was 0.649 for accuracy, 0.659 for precision, 0.662 for recall, 0.645 for f1 score, and 0.707 for the area under the receiver operating characteristic curve (p < 0.001 for all). The result was similar in the support vector machine. The classifier used functional connectivity information from nine connectivities across the motor and somatosensory areas, parietal cortex, temporal cortex, thalamus, and cerebellum. Inter-individual variations in functional connectivity were correlated with the subclinical motor and non-motor symptoms of iRBD patients. CONCLUSIONS: Machine learning-based classifiers using functional connectivity may be useful to evaluate latent brain states in iRBD.

Analysis of the Relationship Between Muscle Tones and Abnormal Postures in a Computational Model.

Patients with Parkinson's disease (PD), a neurodegenerative disorder, exhibit a characteristic posture known as a forward flexed posture. Increased muscle tone is suggested as a possible cause of this abnormal posture. For further analysis, it is necessary to measure muscle tone, but the experimental measurement of muscle tone during standing is challenging. The aim of this study was to examine the hypothesis that "In patients with PD, abnormal postures are those with a small sway at increased muscle tones" using a computational model. The muscle tones of various magnitudes were estimated using the computational model and standing data of patients with PD. The postures with small sway at the estimated muscle tones were then calculated through an optimization method. The postures and sway calculated using the computational model were compared to those of patients with PD. The results showed that the differences in posture and sway between the simulation and experimental results were small at higher muscle tones compared to those considered plausible in healthy subjects by the simulations. This simulation result indicates that the reproduced sway at high muscle tones is similar to that of actual patients with PD and that the reproduced postures with small sway locally at high muscle tones in the simulations are similar to those of patients with PD. The result is consistent with the hypothesis, reinforcing the hypothesis.Clinical relevance- This study implies that improving the increased muscle tone in patients with PD may lead to an improved abnormal posture.

Analysis of abnormal posture in patients with Parkinson's disease using a computational model considering muscle tones.

Patients with Parkinson's disease (PD) exhibit distinct abnormal postures, including neck-down, stooped postures, and Pisa syndrome, collectively termed "abnormal posture" henceforth. In the previous study, when assuming an upright stance, patients with PD exhibit heightened instability in contrast to healthy individuals with disturbance, implying that abnormal postures serve as compensatory mechanisms to mitigate sway during static standing. However, limited studies have explored the relationship between abnormal posture and sway in the context of static standing. Increased muscle tone (i.e., constant muscle activity against the gravity) has been proposed as an underlying reason for abnormal postures. Therefore, this study aimed to investigate the following hypothesis: abnormal posture with increased muscle tone leads to a smaller sway compared with that in other postures, including normal upright standing, under the sway minimization criterion. To investigate the hypothesis, we assessed the sway in multiple postures, which is determined by joint angles, including cases with bended hip joints. Our approach involved conducting forward dynamics simulations using a computational model comprising a musculoskeletal model and a neural controller model. The neural controller model proposed integrates two types of control mechanisms: feedforward control (representing muscle tone as a vector) and feedback control using proprioceptive and vestibular sensory information. An optimization was performed to determine the posture of the musculoskeletal model and the accompanied parameters of the neural controller model for each of the given muscle tone vector to minimize sway. The optimized postures to minimize sway for the optimal muscle tone vector of patients with PD were compared to the actual postures observed in these patients. The results revealed that on average, the joint-angle differences between these postures was <4°, which was less than one-tenth of the typical joint range of motion. These results suggest that patients with PD exhibit less sway in the abnormal posture than in other postures. Thus, adopting an abnormal posture with increased muscle tone can potentially serve as a valid strategy for minimizing sway in patients with PD.

Identifying and reverting the adverse effects of white matter hyperintensities on cortical surface analyses.

The Human Connectome Project (HCP)-style surface-based brain MRI analysis is a powerful technique that allows precise mapping of the cerebral cortex. However, the strength of its surface-based analysis has not yet been tested in the older population that often presents with white matter hyperintensities (WMHs) on T2-weighted (T2w) MRI (hypointensities on T1w MRI). We investigated T1-weighted (T1w) and T2w structural MRI in 43 healthy middle-aged to old participants. Juxtacortical WMHs were often misclassified by the default HCP pipeline as parts of the gray matter in T1w MRI, leading to incorrect estimation of the cortical surfaces and cortical metrics. To revert the adverse effects of juxtacortical WMHs, we incorporated the Brain Intensity AbNormality Classification Algorithm into the HCP pipeline (proposed pipeline). Blinded radiologists performed stereological quality control (QC) and found a decrease in the estimation errors in the proposed pipeline. The superior performance of the proposed pipeline was confirmed using an originally-developed automated surface QC based on a large database. Here we showed the detrimental effects of juxtacortical WMHs for estimating cortical surfaces and related metrics and proposed a possible solution for this problem. The present knowledge and methodology should help researchers identify adequate cortical surface biomarkers for aging and age-related neuropsychiatric disorders.

Changes in functional brain activity patterns associated with computer programming learning in novices.

BACKGROUND: Computer programming, the process of designing, writing, and testing executable computer code, is an essential skill in numerous fields. A description of the neural structures engaged and modified during programming skill acquisition could help improve training programs and provide clues to the neural substrates underlying the acquisition of related skills. METHODS: Fourteen female university students without prior computer programing experience were examined by functional magnetic resonance imaging (fMRI) during the early and late stages of a 5-month 'Computer Processing' course. Brain regions involved in task performance and learning were identified by comparing responses to programming and control tasks during the early and late stages. RESULTS: The accuracy of performing a programming task was significantly improved during the late stage. Various regions of the frontal, temporal, parietal, and occipital cortex as well as several subcortical structures (caudate nuclei and cerebellum) were activated during programming tasks. Brain activity in the right inferior frontal gyrus was greater during the late stage and significantly correlated with improved task performance. Although the left inferior frontal gyrus was also highly active during the programming task, there were no learning-induced changes in activity or a significant correlation between activity and improved task performances. CONCLUSION: Computer programming learning among novices induces functional neuroplasticity within the right inferior frontal gyrus but not the left inferior gyrus (Broca's area).

Interactions across emotional, cognitive and subcortical motor networks underlying freezing of gait.

Freezing of gait (FOG) is a gait disorder affecting patients with Parkinson's disease (PD) and related disorders. The pathophysiology of FOG is unclear because of its phenomenological complexity involving motor, cognitive, and emotional aspects of behavior. Here we used resting-state functional MRI to retrieve functional connectivity (FC) correlated with the New FOG questionnaire (NFOGQ) reflecting severity of FOG in 67 patients with PD. NFOGQ scores were correlated with FCs in the extended basal ganglia network (BGN) involving the striatum and amygdala, and in the extra-cerebellum network (CBLN) involving the frontoparietal network (FPN). These FCs represented interactions across the emotional (amygdala), subcortical motor (BGN and CBLN), and cognitive networks (FPN). Using these FCs as features, we constructed statistical models that explained 40% of the inter-individual variances of FOG severity and that discriminated between PD patients with and without FOG. The amygdala, which connects to the subcortical motor (BGN and CBLN) and cognitive (FPN) networks, may have a pivotal role in interactions across the emotional, cognitive, and subcortical motor networks. Future refinement of the machine learning-based classifier using FCs may clarify the complex pathophysiology of FOG further and help diagnose and evaluate FOG in clinical settings.

Precise motor rhythmicity relies on motor network responsivity.

Rhythmic movements are the building blocks of human behavior. However, given that rhythmic movements are achieved through complex interactions between neural modules, it remains difficult to clarify how the central nervous system controls motor rhythmicity. Here, using a novel tempo-precision trade-off paradigm, we first modeled interindividual behavioral differences in tempo-dependent rhythmicity for various external tempi. We identified 2 behavioral extremes: conventional and paradoxical tempo-precision trade-off types. We then explored the neural substrates of these behavioral differences using task and resting-state functional magnetic resonance imaging. We found that the responsibility of interhemispheric motor network connectivity to tempi was a key to the behavioral repertoire. In the paradoxical trade-off type, interhemispheric connectivity was low at baseline but increased in response to increasing tempo; in the conventional trade-off type, strong baseline connectivity was coupled with low responsivity. These findings suggest that tunable interhemispheric connectivity underlies tempo-dependent rhythmicity control.

Prefrontal network dysfunctions in rapid eye movement sleep behavior disorder.

INTRODUCTION: Resting-state functional connectivity magnetic resonance imaging (rsfcMRI) of rapid eye movement (REM) sleep behavior disorder (RBD) may provide an early biomarker of α-synucleinopathy. However, few rsfcMRI studies have examined cognitive networks. To elucidate brain network changes in RBD, we performed rsfcMRI in patients with polysomnography-confirmed RBD and healthy controls (HCs), with a sufficiently large sample size in each group. METHODS: We analyzed rsfcMRI data from 50 RBD patients and 70 age-matched HCs. Although RBD patients showed no motor signs, some exhibited autonomic and cognitive problems. Several resting-state functional networks were extracted by group independent component analysis from HCs, including the executive-control (ECN), default-mode (DMN), basal ganglia (BGN), and sensory-motor (SMN) networks. Functional connectivity (FC) was compared between groups using dual regression analysis. In the RBD group, correlation analysis was performed between FC and clinical/cognitive scales. RESULTS: Patients with RBD showed reduced striatal-prefrontal FC in ECN, consistent with executive dysfunctions. No abnormalities were found in DMN. In the motor networks, we identified reduced midbrain-pallidum FC in BGN and reduced motor and somatosensory cortex FC in SMN. CONCLUSION: We found abnormal ECN and normal DMN as a possible hallmark of cognitive dysfunctions in early α-synucleinopathies. We replicated abnormalities in BGN and SMN corresponding to subclinical movement disorder of RBD. RsfcMRI may provide an early biomarker of both cognitive and motor network dysfunctions of α-synucleinopathies.

Brain/MINDS beyond human brain MRI project: A protocol for multi-level harmonization across brain disorders throughout the lifespan.

Psychiatric and neurological disorders are afflictions of the brain that can affect individuals throughout their lifespan. Many brain magnetic resonance imaging (MRI) studies have been conducted; however, imaging-based biomarkers are not yet well established for diagnostic and therapeutic use. This article describes an outline of the planned study, the Brain/MINDS Beyond human brain MRI project (BMB-HBM, FY2018 ~ FY2023), which aims to establish clinically-relevant imaging biomarkers with multi-site harmonization by collecting data from healthy traveling subjects (TS) at 13 research sites. Collection of data in psychiatric and neurological disorders across the lifespan is also scheduled at 13 sites, whereas designing measurement procedures, developing and analyzing neuroimaging protocols, and databasing are done at three research sites. A high-quality scanning protocol, Harmonization Protocol (HARP), was established for five high-quality 3 T scanners to obtain multimodal brain images including T1 and T2-weighted, resting-state and task functional and diffusion-weighted MRI. Data are preprocessed and analyzed using approaches developed by the Human Connectome Project. Preliminary results in 30 TS demonstrated cortical thickness, myelin, functional connectivity measures are comparable across 5 scanners, suggesting sensitivity to subject-specific connectome. A total of 75 TS and more than two thousand patients with various psychiatric and neurological disorders are scheduled to participate in the project, allowing a mixed model statistical harmonization. The HARP protocols are publicly available online, and all the imaging, demographic and clinical information, harmonizing database will also be made available by 2024. To the best of our knowledge, this is the first project to implement a prospective, multi-level harmonization protocol with multi-site TS data. It explores intractable brain disorders across the lifespan and may help to identify the disease-specific pathophysiology and imaging biomarkers for clinical practice.

Effects of Field-Map Distortion Correction on Resting State Functional Connectivity MRI.

Magnetic field inhomogeneities cause geometric distortions of echo planar images used for functional magnetic resonance imaging (fMRI). To reduce this problem, distortion correction (DC) with field map is widely used for both task and resting-state fMRI (rs-fMRI). Although DC with field map has been reported to improve the quality of task fMRI, little is known about its effects on rs-fMRI. Here, we tested the influence of field-map DC on rs-fMRI results using two rs-fMRI datasets derived from 40 healthy subjects: one with DC (DC+) and the other without correction (DC-). Independent component analysis followed by the dual regression approach was used for evaluation of resting-state functional connectivity networks (RSN). We also obtained the ratio of low-frequency to high-frequency signal power (0.01-0.1 Hz and above 0.1 Hz, respectively; LFHF ratio) to assess the quality of rs-fMRI signals. For comparison of RSN between DC+ and DC- datasets, the default mode network showed more robust functional connectivity in the DC+ dataset than the DC- dataset. Basal ganglia RSN showed some decreases in functional connectivity primarily in white matter, indicating imperfect registration/normalization without DC. Supplementary seed-based and simulation analyses supported the utility of DC. Furthermore, we found a higher LFHF ratio after field map correction in the anterior cingulate cortex, posterior cingulate cortex, ventral striatum, and cerebellum. In conclusion, field map DC improved detection of functional connectivity derived from low-frequency rs-fMRI signals. We encourage researchers to include a DC step in the preprocessing pipeline of rs-fMRI analysis.

Predictive factors for the outcome of multidisciplinary treatments in chronic low back pain at the first multidisciplinary pain center of Japan.

[Purpose] Multidisciplinary treatments are recommended for treatment of chronic low back pain. The aim of this study was to show the associations among multidisciplinary treatment outcomes, pretreatment psychological factors, self-reported pain levels, and history of pain in chronic low back pain patients. [Subjects and Methods] A total of 221 chronic low back pain patients were chosen for the study. The pretreatment scores for the 10-cm Visual Analogue Scale, Hospital Anxiety and Depression Scale, Pain Catastrophizing Scale, Short-Form McGill Pain Questionnaire, Pain Disability Assessment Scale, pain drawings, and history of pain were collected. The patients were divided into two treatment outcome groups a year later: a good outcome group and a poor outcome group. [Results] One-hundred eighteen patients were allocated to the good outcome group. The scores for the Visual Analogue Scale, Pain Disability Assessment Scale, and affective subscale of the Short-Form McGill Pain Questionnaire and number of nonorganic pain drawings in the good outcome group were significantly lower than those in the poor outcome group. Duration of pain in the good outcome group was significantly shorter than in the poor outcome group. [Conclusion] These findings help better predict the efficacy of multidisciplinary treatments in chronic low back pain patients.