Structural neuroplasticity in computer programming beginners.

We examined the structural neuroplastic changes associated with the learning of computer programming in university students with no previous programming experience. They participated in a 15-week course (26 lessons) on the "Processing" computer programming language. We have conducted a longitudinal analysis of gray matter volume (GMV) in the magnetic resonance images obtained before and after learning computer programming. Significant neuroplastic changes appeared in the following 8 sites: the left frontal pole; the right frontal pole; the right medial frontal gyrus; the left cuneus; the left lateral cerebellum (posterior lobule and tuber); the medial cerebellum (uvula and tonsil); the right pallidum; and the left pallidum. The amount of change in the GMV of the right frontal pole correlated positively with the final product score. Furthermore, the amount of change in the GMV of the right medial frontal gyrus and the bilateral pallidum correlated positively with the test scores. Thus, the right frontal pole was presumably associated with the function of persistent attempts to accomplish tasks (goal achievement-related function). The right medial frontal gyrus and the bilateral pallidum were presumably related to deduction and reward functions, respectively. Therefore, multiple brain regions appear to be involved in programming learning through different functions.

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.

The first Japanese case of leukodystrophy with ovarian failure arising from novel compound heterozygous AARS2 mutations.

Even now, only a portion of leukodystrophy patients are correctly diagnosed, though various causative genes have been identified. In the present report, we describe a case of adult-onset leukodystrophy in a woman with ovarian failure. By whole-exome sequencing, a compound heterozygous mutation consisting of NM_020745.3 (AARS2_v001):c.1145C>A and NM_020745.3 (AARS2_v001):c.2255+1G>A was identified. Neither of the mutations has been previously reported, and this is the first report of alanyl-transfer RNA synthetase 2 mutation in Asia. We anticipate that further studies of the molecular basis of leukodystrophy will provide insight into its pathogenesis and hopefully lead to sophisticated diagnostic and treatment strategies.

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.

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).

Comparison of Phase Synchronization Measures for Identifying Stimulus-Induced Functional Connectivity in Human Magnetoencephalographic and Simulated Data.

Phase synchronization measures are widely used for investigating inter-regional functional connectivity (FC) of brain oscillations, but which phase synchronization measure should be chosen for a given experiment remains unclear. Using neuromagnetic brain signals recorded from healthy participants during somatosensory stimuli, we compared the performance of four phase synchronization measures, imaginary part of phase-locking value, imaginary part of coherency (ImCoh), phase lag index and weighted phase lag index (wPLI), for detecting stimulus-induced FCs between the contralateral primary and ipsilateral secondary somatosensory cortices. The analyses revealed that ImCoh exhibited the best performance for detecting stimulus-induced FCs, followed by the wPLI. We found that amplitude weighting, which is related to computing both ImCoh and wPLI, effectively attenuated the influence of noise contamination. A simulation study modeling noise-contaminated periodograms replicated these findings. The present results suggest that the amplitude-dependent measures, ImCoh followed by wPLI, may have the advantage in detecting stimulus-induced FCs.

Leukoencephalopathy with a case of heterozygous POLG mutation mimicking mitochondrial neurogastrointestinal encephalomyopathy (MNGIE).

Diseases due to mutations of polymerase γ (POLG) usually present with progressive external ophthalmoplegia. However, a few studies have been reported on POLG1 mutations with the mitochondrial neurogastrointestinal encephalomyopathy (MNGIE)-like phenotype. All cases with POLG1 mutations mimicking MNGIE have never shown leukoencephalopathy on brain magnetic resonance imaging (MRI) or demyelinating polyneuropathy. We present a 26-year-old male with gait disturbance, recurrent bowel obstruction, peripheral neuropathy, ophthalmoplegia or ptosis, which represented MNGIE phenotype. Though he displayed demyelinating peripheral neuropathy or leukoencephalopathy on brain MRI, genetic analysis revealed heterozygous mutation in POLG1 gene. We report for the first time two newly characteristics in our patient with heterozygous POLG1 mutations with the MNGIE-like phenotype: leukoencephalopathy and demyelinating polyneuropathy.

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.