Comprehensive evaluation of pipelines for diagnostic biomarkers of major depressive disorder using multi-site resting-state fMRI datasets

Takahara, Y.; Kashiwagi, Y.; Tokuda, T.; Yoshiomoto, J.; Sakai, Y.; Yamashita, A.; Yoshioka, T.; Takahashi, H.; Mizuta, H.; Kasai, K.; Kunimitsu, A.; Okada, N.; Itai, E.; Shinzato, H.; Yokoyama, S.; Masuda, Y.; Mitsuyama, Y.; Okada, G.; Okamoto, Y.; Itahashi, T.; Ota, H.; Hashimoto, R.-i.; Harada, K.; Yamagata, H.; Matsubara, T.; Matsuo, K.; Tanaka, S. C.; Imamizu, H.; Ogawa, K.; Momosaki, S.; Kawato, M.; Yamashita, O.

doi:10.1101/2024.03.17.584538

Home/Archive/bioRxiv

bioRxiv · 10.1101/2024.03.17.584538

Comprehensive evaluation of pipelines for diagnostic biomarkers of major depressive disorder using multi-site resting-state fMRI datasets

View original source ↗Export metadata ↓

Abstract

The objective diagnostic and stratification biomarkers developed with resting-state functional magnetic resonance imaging (rs-fMRI) data are expected to contribute to more effective treatment for mental disorders. Unfortunately, there are currently no widely accepted biomarkers, partially due to the large variety of analysis pipelines for developing them. In this study we comprehensively evaluated analysis pipelines using a large-scale, multi-site fMRI dataset for major depressive disorder (MDD) (1162 participants from eight imaging sites). We explored the combinations of options in four subprocesses of analysis pipelines: six types of brain parcellation, four types of estimations of functional connectivity (FC), three types of site difference harmonization, and five types of machine learning methods. 360 different MDD diagnostic biomarkers were constructed using the SRPBS dataset acquired with unified protocols (713 participants from four imaging sites) as a discovery dataset and evaluated with datasets from other projects acquired with heterogeneous protocols (449 participants from four imaging sites) for independent validation. To identify the optimal options regardless of the discovery dataset, we repeated the same procedure after swapping the roles of the two datasets. We found pipelines that included Glassers parcellation, tangent-covariance, no harmonization, and non-sparse machine learning methods tended to result in high classification performance. The diagnosis results of the top 10 biomarkers showed high similarity, and weight similarity was also observed between eight of the biomarkers, except two that used both data-driven parcellation and FC computation. We applied the top 10 pipelines to the datasets of other mental disorders (autism spectral disorder: ASD and schizophrenia: SCZ) and eight of the ten biomarkers showed sufficient classification performances for both disorders, except two pipelines that included Pearson correlation, ComBat harmonization and random forest classifier combination. HighlightsO_LIWe evaluated the analysis pipelines of rsFC biomarker development. C_LIO_LIFour subprocesses in them were investigated with two multi-site datasets. C_LIO_LIGlassers parcellation, tangent covariance, and non-sparse methods were preferred. C_LIO_LIThe weight patterns of eight of the top 10 biomarkers showed high commonality. C_LIO_LIEight of the top 10 pipelines were successful for developing SCZ/ASD biomarkers. C_LI

Explore related subjects

neuroscience Preprint

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Takahara, Y., Kashiwagi, Y., Tokuda, T., Yoshiomoto, J., Sakai, Y., Yamashita, A., Yoshioka, T., Takahashi, H., Mizuta, H., Kasai, K., Kunimitsu, A., Okada, N., Itai, E., Shinzato, H., Yokoyama, S., Masuda, Y., Mitsuyama, Y., Okada, G., Okamoto, Y., Itahashi, T., Ota, H., Hashimoto, R.-i., Harada, K., Yamagata, H., Matsubara, T., Matsuo, K., Tanaka, S. C., Imamizu, H., Ogawa, K., Momosaki, S., Kawato, M., Yamashita, O.. 2024-03-19. Comprehensive evaluation of pipelines for diagnostic biomarkers of major depressive disorder using multi-site resting-state fMRI datasets. https://doi.org/10.1101/2024.03.17.584538

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Neurodegeneration-inducing macromolecules exit the brain via nanovascular conduits formed by reticular fibroblasts

Accumulation of proteins such as amyloid beta (Abeta), hyperphosphorylated tau and alpha-synuclein within the brain alters neural information processing and causes neurodegeneration(1-3), but how toxic solutes are cleared from the brain remains highly controversial(4,5). Proposed exit routes include efflux across endothelial cells into the blood(6,7), and movement to the pial surface via vasomotion-induced pumping along spaces within arteriolar smooth muscle(8) or via outflow along the perivascular space of ascending venules promoted by water flux through astrocytes (the glymphatic system(9)). From the pial surface of the brain, drainage may continue to dural lymphatics, along the outer sheaths of exiting cranial nerves and across the cribriform plate(10-14). We now report the presence, in mice and humans, of 2 micron diameter conduits that remove fluorescently labelled tau and Abeta from the brain. These conduits form a spatially-organised mesh within the walls of penetrating arterioles and pial arteries, and around the surface of ascending venules and deep cerebral and pial veins. They course through the pial and arachnoid layers to span the CSF space, wrapping the brain and cranial nerves. They are formed of reticular fibroblasts, which label for VE-cadherin(15) and PDGFRalpha(16), the lymphatic markers(17) podoplanin, VEGFR3 and Prox1, and reticular fibroblast extracellular matrix components collagen I and VI(16,18-20). Parenchymal tau drains from the brain at a similar rate via arteriolar conduits and via conduits around venules, arguing against preferential removal by a glymphatic mechanism. In Alzheimer's disease model mice, Abeta is seen traversing these lymph node-like conduits. Modulation of molecular transfer via this route may accelerate or delay cognitive decline, and slowed transfer from arteriolar to pial-arachnoid conduits may initiate cerebral amyloid angiopathy.

neuroscience↗

bioRxiv2026

Analysis of the influence of gradual changes in matrix sentence similarity on neural envelope tracking

Neural tracking of speech is a well-established phenomenon in neuroscience. However, for speech signals with a fixed structure, significant correlations between speech envelopes and neurophysiological representations occur even for unheard sentences. We exploit a structured speech-in-noise matrix hearing test (Oldenburger Sentence Test, OLSA) to systematically quantify the relationship between acoustic sentence similarity and neural tracking. Simultaneous magnetoencephalography (MEG) and 76-channel electroencephalography (EEG) data, including 16 channels positioned directly around the ears (ear-EEG), were recorded from 21 young adults with normal hearing during the presentation of clean-speech audiobooks and OLSA sentences at six signal-to-noise ratios. A linear decoder trained on audiobooks reconstructed OLSA sentence envelopes. Reconstruction accuracies were compared using a linear mixed model across heard (matched) and unheard (mismatched) sentences of varying acoustic similarity. Significant reconstruction accuracies were achieved across MEG, EEG, and ear-EEG for both matched and mismatched sentences. For mismatched sentences, these accuracies gradually increased with their acoustic similarity to the heard speech data. The high similarity between sentences, which is especially prominent in matrix tests, can cause significant spurious tracking for mismatched stimuli. This effect can reach levels comparable to those of matched sentences and can be mistaken for true neural tracking. Robust neural tracking across modalities further supported the established viability of ear-EEG compared to whole-head systems.

neuroscience↗

bioRxiv2026

Seizures and tauopathy following neurotrauma are mediated by prion protein and metabotropic glutamate receptor 5

Traumatic brain injury (TBI) is one of the world's leading causes of death and disability and a major risk factor for dementias. The primary dementia associated with TBI is chronic traumatic encephalopathy (CTE), a neurodegenerative disease classified as a tauopathy, in which toxic tau molecules lead to disease pathologies and degeneration. The processes that lead to tauopathy and subsequent dementia after TBI remain unclear. Here, we built upon the finding that seizures after TBI may be a mechanism leading to tauopathy, by dissecting the functions of the metabotropic glutamate receptor 5 - cellular prion protein (mGluR5-PrPC) pathway. We delivered TBI to larval in a blast paradigm, and quantified aggregation of Tau via a genetically-encoded Tau-GFP fusion reporter. Zebrafish larvae lacking prp2 (homolog of mammalian cellular Prion Protein, PrPC) displayed a 168% increase in post-traumatic seizures activity after TBI. An mGluR5 agonist (CHPG) reduced post-traumatic seizures, whereas an mGluR5 antagonist (MPEP) increased post-traumatic seizures. Moreover, agonizing mGluR5 reduced tau aggregation and antagonizing mGluR5 increased tau burden. Larvae seizing from convulsants, rather than TBI, were treated with CHPG/MPEP and provided a similar pattern of outcomes, suggesting seizures may be a factor needed for mGluR5 activity to influence tau aggregation. The PrPC-mGluR5 pathway is proposed as one candidate pathomechanism linking TBI to subsequent seizures and tauopathy, and thus it warrants investigation as a target for prophylactic interventions.

neuroscience↗