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Background and Purpose: An early and accurate diagnosis of Dementia with Lewy bodies (DLB) is critical because treatments and prognosis of DLB are different from Alzheimer's disease (AD). This study was carried out in Japan to validate an Electroencephalography (EEG)-derived machine learning algorithm for discriminating DLB from AD which developed based on a database of EEG records from two different European countries., Methods: In a prospective multicenter study, patients with probable DLB or with probable AD were enrolled in a 1:1 ratio. A continuous EEG segment of 150 seconds was recorded, and the EEG data was processed using MC-004, the EEG-based machine learning algorithm, with all clinical information blinded except for age and gender., Results: Eighteen patients with probable DLB and 21 patients with probable AD were the included for the analysis. The performance of MC-004 differentiating probable DLB from probable AD was 72.2% (95% CI 46.5-90.3%) for sensitivity, 85.7% (63.7-97.0%) for specificity, and 79.5% (63.5-90.7%) for accuracy. When limiting to subjects taking ≤5 mg donepezil, the sensitivity was 83.3% (95% CI 51.6-97.9), the specificity 89.5% (66.9-98.7), and the accuracy 87.1% (70.2-96.4)., Conclusions: MC-004, the EEG-based machine learning algorithm, was able to discriminate between DLB and AD with fairly high accuracy. MC-004 is a promising biomarker for DLB, and has the potential to improve the detection of DLB in a diagnostic process., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: YS, MS,TA and EM report grants and personal fees from Mentis Cura (https://www.mentiscura.com) during the conduct of the study. KShig, KShin, YA, HK, TB and MH report grants from Mentis Cura, during the conduct of the study. KJ is an employee of Mentis Cura. All the authors have sufficiently participated in the work and take responsibility for the content and agree to transfer copyright to PLOS. We disclose potential conflicts of interest and describe their financial support. This does not alter our adherence to PLOS ONE policies on sharing data and materials. Our manuscript contains no extractions from other copyrighted works. We confirm that this manuscript has not been accepted for publication elsewhere, is not being considered for publication elsewhere, and does not duplicate previously published material. I confirm that all the authors consent to publication of this manuscript.

Tinnitus is the perception of phantom sound without an external auditory stimulus. Using neuroimaging techniques, such as positron emission tomography, electroencephalography, magnetoencephalography, and functional magnetic resonance imaging (fMRI), many studies have demonstrated that abnormal functions of the central nervous system are closely associated with tinnitus. In our previous research, we reported using resting-state fMRI that several brain regions, including the rectus gyrus, cingulate gyrus, thalamus, hippocampus, caudate, inferior temporal gyrus, cerebellar hemisphere, and medial superior frontal gyrus, were associated with tinnitus distress and loudness. To reconfirm these results and probe target regions for repetitive transcranial magnetic stimulation (rTMS), we investigated the regional cerebral blood flow (rCBF) between younger tinnitus patients (<60 years old) and the age-matched controls using single-photon emission computed tomography and easy Z-score imaging system. Compared with that of controls, the rCBF of tinnitus patients was significantly lower in the bilateral medial superior frontal gyri, left middle occipital gyrus and significantly higher in the bilateral cerebellar hemispheres and vermis, bilateral middle temporal gyri, right fusiform gyrus. No clear differences were observed between tinnitus patients with normal and impaired hearing. Regardless of the assessment modality, similar brain regions were identified as characteristic in tinnitus patients. These regions are potentially involved in the pathophysiology of chronic subjective tinnitus.

Subjective tinnitus is characterized by the perception of phantom sound without an external auditory stimulus. We hypothesized that abnormal functionally connected regions in the central nervous system might underlie the pathophysiology of chronic subjective tinnitus. Statistical significance of functional connectivity (FC) strength is affected by the regional autocorrelation coefficient (AC). In this study, we used resting-state functional MRI (fMRI) and measured regional mean FC strength (mean cross-correlation coefficient between a region and all other regions without taking into account the effect of AC (rGC) and with taking into account the effect of AC (rGCa) to elucidate brain regions related to tinnitus symptoms such as distress, depression and loudness. Consistent with previous studies, tinnitus loudness was not related to tinnitus-related distress and depressive state. Although both rGC and rGCa revealed similar brain regions where the values showed a statistically significant relationship with tinnitus-related symptoms, the regions for rGCa were more localized and more clearly delineated the regions related specifically to each symptom. The rGCa values in the bilateral rectus gyri were positively correlated and those in the bilateral anterior and middle cingulate gyri were negatively correlated with distress and depressive state. The rGCa values in the bilateral thalamus, the bilateral hippocampus, and the left caudate were positively correlated and those in the left medial superior frontal gyrus and the left posterior cingulate gyrus were negatively correlated with tinnitus loudness. These results suggest that distinct brain regions are responsible for tinnitus symptoms. The regions for distress and depressive state are known to be related to depression, while the regions for tinnitus loudness are known to be related to the default mode network and integration of multi-sensory information.

Slow (<0.1 Hz) oscillatory activity in the human brain, as measured by functional magnetic imaging, has been used to identify neural networks and their dysfunction in specific brain diseases. Its intrinsic properties may also be useful to investigate brain functions. We investigated the two functional maps: variance and first order autocorrelation coefficient (r(1)). These two maps had distinct spatial distributions and the values were significantly different among the subdivisions of the precuneus and posterior cingulate cortex that were identified in functional connectivity (FC) studies. The results reinforce the functional segregation of these subdivisions and indicate that the intrinsic properties of the slow brain activity have physiological relevance. Further, we propose a sample size (degree of freedom) correction when assessing the statistical significance of FC strength with r(1) values, which enables a better understanding of the network changes related to various brain diseases.