Late combination shows that MEG adds to MRI in classifying MCI versus controls

Vaghari, Delshad; Kabir, Ehsanollah; Henson, Richard N.

doi:10.1016/j.neuroimage.2022.119054

Cited by 15 publications

(14 citation statements)

References 77 publications

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“…Additionally, combining these biomarkers with other imaging techniques, such as MRS and PET (Ranasinghe et al 2022;Vaghari, Kabir, and Henson 2022;Popescu et al 2020), will allow us to gain a better insight into the mechanisms of E/I by leveraging the strengths of each method.…”

Section: Novel Noninvasive E/i Proxy Markersmentioning

confidence: 99%

Combining Aperiodic 1/f Slopes and Brain Simulation: An EEG/MEG Proxy Marker of Excitation/Inhibition Imbalance in Alzheimer’s Disease

Martínez‐Cañada

Pérez-Valero

Minguillón

et al. 2022

Preprint

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Accumulation and interaction of amyloid-beta (Aβ) and tau proteins during progression of Alzheimer's disease (AD) are shown to tilt neuronal circuits away from balanced excitation/inhibition (E/I). Current available techniques for noninvasive interrogation of E/I in the intact human brain, e.g., magnetic resonance spectroscopy (MRS), are highly restrictive (i.e., limited spatial extent), have low temporal and spatial resolution and suffer from the limited ability to distinguish accurately between different neurotransmitters complicating its interpretation. As such, these methods alone offer an incomplete explanation of E/I. Recently, the aperiodic component of neural power spectrum, often referred to in the literature as the '1/f slope', has been described as a promising and scalable biomarker that can track disruptions in E/I potentially underlying a spectrum of clinical conditions, such as autism, schizophrenia, or epilepsy, as well as developmental E/I changes as seen in aging. Using 1/f slopes from resting-state spectral data and computational modelling we developed a new method for inferring E/I alterations in AD. We tested our method on recent freely and publicly available electroencephalography (EEG) and magnetoencephalography (MEG) datasets of patients with AD or prodromal disease and demonstrated the method's potential for uncovering regional patterns of abnormal excitatory and inhibitory parameters. Our results provide a general framework for investigating circuit-level disorders in AD and developing therapeutic interventions that aim to restore the balance between excitation and inhibition.

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Section: Novel Noninvasive E/i Proxy Markersmentioning

confidence: 99%

Combining Aperiodic 1/f Slopes and Brain Simulation: An EEG/MEG Proxy Marker of Excitation/Inhibition Imbalance in Alzheimer’s Disease

Martínez‐Cañada

Pérez-Valero

Minguillón

et al. 2022

Preprint

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“…Notably, to date, only one published study used BioFIND data for classification (Vaghari et al, 2022b). In that study, extracted features represented the mean grey matter volume across voxels within 110 cortical regions of interest, and various classification algorithms (including SVM, KNN, RF, and MLP) yielded classification accuracies ranging from 0.66-0.76; lower than those obtained here, rendering the current approach promising.…”

Section: Discussionmentioning

confidence: 75%

“…A more recent resource is the BioFIND dataset, which includes T1-weighted scans from N~320 participants grouped into MCI patients and HC (Vaghari et al, 2022a). Whilst the latter BioFIND dataset was launched only recently, and for the time being only yielded two publications (Bruña et al, 2022;Vaghari et al, 2022b), the ADNI dataset was extensively studied over the last decades, yielding, according to the project's website (https://adni.loni.usc.edu/), more than 3,000 published peer-reviewed articles. In one, relatively early but comprehensive examination, Cuingnet et al (2011) used 10 different methods for feature extraction from structural MRIs (including voxel-based methods, cortical thickness methods, and hippocampus-based methods) in order to classify AD patients, MCI patients, and HC.…”

Section: Figures:mentioning

confidence: 99%

“…Various types of structural features extracted from MRI images have been used in the past (e.g., Gerardin et al, 2009;Klöppel et al, 2008;Vemuri et al, 2008). In some cases, the features were defined at the level of the MRI voxel throughout the entire brain (e.g., Klöppel et al, 2008) or for specific key regions (e.g., Gerardin et al, 2009), whereas in others, preliminary steps were taken to reduce the dimensionality of the feature space using different types of feature extraction and selection methods, including smoothing, voxel-downsampling, and parcellation (e.g., Fan et al, 2008aFan et al, , 2008bMagnin et al, 2009;Vaghari et al, 2022b;Vemuri et al, 2008). Novel methods for feature extractions are constantly being developed, aiming to capture additional fine-grained aspects of brain structures.…”

Section: Figures:mentioning

confidence: 99%

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Structural complexity of brain regions in mild cognitive impairment and Alzheimer’s disease

Tibon

Madan

Vaghari

et al. 2023

Preprint

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Early detection of Alzheimers disease (AD) has been a major focus of current research efforts to guide interventions at the earliest stages of the disease. Subtle changes to the brain might be observed with neuroimaging techniques, even before symptoms surface. We interrogated brain images obtained with Magnetic Resonance Imaging (MRI) from two large-scale dementia datasets (namely, ADNI and BioFIND) to establish the utility of fractal dimensionality (FD)-a relatively understudied measure that estimates the complexity of 3D structures (in this case, brain regions)-for the detection of AD. We show that FD can be used to detect group differences between patients and healthy controls, with the former showing significantly reduced complexity across multiple brain regions. Furthermore, these measures were successful when used as features for individual-based classification and were highly consistent across the two datasets. Finally, the contribution of specific brain regions to individual-based classification adhered to previous literature on the properties of the brain memory network. Taken together, the study offers novel and interpretable evidence for the utility of FD for the detection of AD.

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“…We replicated our findings on another multi-modal dataset called MOUS (Mother Of all Unification Studies; n=189) (Schoffelen et al, 2019) with resting state data acquired during both modalities. Ours is one of the first studies to demonstrate the connection between the multimodal (MEG and rsfMRI) measures of cognitive health (Engemann et al, 2020; Vaghari et al, 2022; Xifra-Porxas et al, 2021)…”

Section: Introductionmentioning

confidence: 99%

Default Mode and Dorsal Attention Network functional connectivity associated with alpha and beta peak frequency in individuals

Tripathi

Somers

2023

Preprint

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Alpha- and Beta-frequency oscillatory waves are evident in resting-state electro- and magneto-encephalography (EEG, MEG). Higher peak frequencies for Alpha and Beta are associated with greater cognitive health. Aging slows down the alpha and beta waves which are also affected by mental states, disorders such as ADHD, and sleep deprivation. Functional magnetic resonance imaging (fMRI) of resting-state brain activity reveals anti-correlation of very low frequency (< 0.1 Hz) fluctuations between two distributed cerebral cortical networks, the Dorsal Attention Network (DAN) and the Default Mode Network (DMN). DAN activation is related to attentional demands in extrinsic tasks, whereas DMN is associated with mind wandering, episodic memory retrieval, and intrinsic processing. Prior research has found that higher DAN-DMN anticorrelation is related to greater cognitive and mental health. Here, we investigated whether these two measures of cognitive and mental health are related to each other within individuals. We investigated resting-state Functional Connectivity (rsFC) between the DMN and DAN using fMRI and alpha and beta peaks from MEG using two large datasets (n=89 and n=189). We found that more robust anti-correlations between DMN and DAN regions are related to higher peak frequencies of alpha and beta rhythms in the brain. Subjects with higher alpha peak frequencies also show stronger positive within-network connectivity in both the DMN and DAN networks. Females show stronger correspondence between the two measures as compared to males. This association between two non-invasive cognitive neuroscience modalities adds to the growing literature on the biomarkers of cognitive and mental health.

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Late combination shows that MEG adds to MRI in classifying MCI versus controls

Cited by 15 publications

References 77 publications

Combining Aperiodic 1/f Slopes and Brain Simulation: An EEG/MEG Proxy Marker of Excitation/Inhibition Imbalance in Alzheimer’s Disease

Combining Aperiodic 1/f Slopes and Brain Simulation: An EEG/MEG Proxy Marker of Excitation/Inhibition Imbalance in Alzheimer’s Disease

Structural complexity of brain regions in mild cognitive impairment and Alzheimer’s disease

Default Mode and Dorsal Attention Network functional connectivity associated with alpha and beta peak frequency in individuals

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