Biology subjects

Khaligh-Razavi, S.-M.

Publications and source records attributed to Khaligh-Razavi, S.-M..

8 recordsLinked to original sources

bioRxiv2022

A Multivariate Comparison of EEG and fMRI to ECoG Using Visual Object Representations in Humans

1.Today most neurocognitive studies in humans employ the non-invasive neuroimaging techniques fMRI and EEG. However, how the data provided by fMRI and EEG relate exactly to the underlying neural activity remains incompletely understood. Here, we pursued this open question by relating EEG and fMRI data at the level of neural population codes using multivariate pattern analysis. For this, we recorded EEG and fMRI data while participants viewed everyday objects in different viewing conditions, and then related the data to ECoG data recorded for the same stimulus set. The comparison of EEG and ECoG data showed that object category signals emerge swiftly in the visual system and can be detected by both EEG and ECoG at similar temporal delays after stimulus onset. The correlation between EEG and ECoG reduces and onset latencies increase when object representations tolerant to changes in scale and orientation are considered. The comparison of fMRI and ECoG overall revealed a tighter relationship in occipital than in temporal regions, related to differences in fMRI signal-to-noise ratio. Together, our results reveal a complex relationship between fMRI, EEG and ECoG signals at the level of population codes that critically depends on the time point after stimulus onset, the region investigated, and the visual contents used.

neuroscience↗

bioRxiv2022

A Multimodal Neuroimaging Dataset to Study Spatiotemporal Dynamics of Visual Processing in Humans

We describe structural and multimodal functional neuroimaging data collected from 21 healthy volunteers. Functional magnetic resonance images (fMRI) and electroencephalography (EEG) signals were acquired in separate sessions from the same individuals while they were performing a visual one-back repetition task. During functional sessions, participants were presented with images from five categories, including animals, chairs, faces, fruits, and vehicles. The stimulus set and experimental parameters were chosen to be similar to that of an available electrocorticography (ECoG) dataset, therefore creating a unique opportunity to study vision in humans with multiple complementary neuroimaging modalities. Individual-specific head models can be constructed for each participant using T1-weighted MPRAGE images and the recorded positions of the EEG electrodes. By combining the three functional modalities and the structural data, this dataset provides a unique setting to explore spatiotemporal dynamics of invariant object recognition in humans. This multimodal data can also be used to develop new methods for combining fMRI and electrophysiological modalities to come up with more accurate spatiotemporally resolved maps of brain function, which is inaccessible by any of the modalities alone.

neuroscience↗

bioRxiv2021

Validity and cultural generalisability of a 5-minute AI-based, computerised cognitive assessment in Mild Cognitive Impairment and Alzheimer's Dementia

INTRODUCTIONEarly detection and monitoring of mild cognitive impairment (MCI) and Alzheimers Disease (AD) patients are key to tackling dementia and providing benefits to patients, caregivers, healthcare providers and society. We developed the Integrated Cognitive Assessment (ICA); a 5-minute, language independent computerised cognitive test that employs an Artificial Intelligence (AI) model to improve its accuracy in detecting cognitive impairment. In this study, we aimed to evaluate the generalisability of the ICA in detecting cognitive impairment in MCI and mild AD patients. METHODSWe studied the ICA in a total of 230 participants. 95 healthy volunteers, 80 MCI, and 55 participants with mild AD completed the ICA, the Montreal Cognitive Assessment (MoCA) and Addenbrookes Cognitive Examination (ACE) cognitive tests. RESULTSThe ICA demonstrated convergent validity with MoCA (Pearson r = 0.58, p<0.0001) and ACE (r = 0.62, p<0.0001). The ICA AI model was able to detect cognitive impairment with an AUC of 81% for MCI patients, and 88% for mild AD patients. The AI model demonstrated improved performance with increased training data and showed generalisability in performance from one population to another. The ICA correlation of 0.17 (p=0.01) with education years is considerably smaller than that of MoCA (r=0.34, p<0.0001) and ACE (r=0.41, p<0.0001) which displayed significant correlations. In a separate study the ICA demonstrated no significant practice effect observed over the duration of the study. DISCUSSIONThe ICA can support clinicians by aiding accurate diagnosis of MCI and AD and is appropriate for large-scale screening of cognitive impairment. The ICA is unbiased by differences in language, culture and education.

neuroscience↗

bioRxiv2020

Temporal dynamics of animacy categorization in the brain of patients with mild cognitive impairment

Electroencephalography (EEG) has been commonly used to measure brain alterations in Alzheimers Disease (AD). However, reported changes are limited to those obtained from using univariate measures, including activation level and frequency bands. To look beyond the activation level, we used multivariate pattern analysis (MVPA) to extract patterns of information from EEG responses to images in an animacy categorization task. Comparing healthy controls (HC) with patients with mild cognitive impairment (MCI), we found that the neural speed of animacy information processing is decreased in MCI patients. Moreover, we found critical time-points during which the representational pattern of animacy for MCI patients was significantly discriminable from that of HC, while the activation level remained unchanged. Together, these results suggest that the speed and pattern of animacy information processing provide clinically useful information as a potential biomarker for detecting early changes in MCI and AD patients.

neuroscience↗

bioRxiv2020

Noise ceiling on the crossvalidated performance of reweighted models of representational dissimilarity: Addendum to Khaligh-Razavi & Kriegeskorte (2014)

An error was made in including noise ceilings for human data in Khaligh-Razavi and Kriegeskorte (2014). For comparability with the macaque data, human data were averaged across participants before analysis. Therefore the noise ceilings indicating variability across human participants do not accurately depict the upper bounds of possible model performance and should not have been shown. Creating noise ceilings appropriate for the fitted models is not trivial. Below we present a method for doing this, and the results obtained with this new method. The corrected results differ from the original results in that the best-performing model (weighted combination of AlexNet layers and category readouts) does not reach the lower bound of the noise ceiling. However, the best-performing model is not significantly below the lower bound of the noise ceiling. The claim that the model "fully explains" the human IT data appears overstated. All other claims of the paper are unaffected.

neuroscience↗

bioRxiv2019

A self-administered, artificial intelligence (AI) platform for cognitive assessment in multiple sclerosis (MS)

BackgroundCognitive impairment is common in patients with MS. Accurate and repeatable measures of cognition have the potential to be used as a marker of disease activity. We developed a 5-minute computerized test to measure cognitive dysfunction in patients with MS. The proposed test -named Integrated Cognitive Assessment (ICA)- is self-administered and language-independent.\n\nObjectiveTo determine ICAs validity as a digital biomarker for assessing cognitive performance in MS.\n\nMethods91 MS patients and 83 healthy controls (HC) took part in substudy 1, in which each participant took the ICA test and the Brief International Cognitive Assessment for MS (BICAMS). We assessed ICAs test-retest reliability, its correlation with BICAMS, its sensitivity to discriminate patients with MS from the HC group, and its accuracy in detecting cognitive dysfunction. In substudy 2, we recruited 48 MS patients, and examined the association between the level of serum neurofilament light (NfL) in these patients and their ICA scores.\n\nResultsICA demonstrated excellent test-retest reliability (r=0.94), with no learning bias (i.e. no significant practice effect); and had high level of convergent validity with BICAMS. ICA was sensitive in discriminating the MS patients from the HC group, and demonstrated a high accuracy (AUC = 95%) in discriminating cognitively normal from cognitively impaired participants. Additionally, we found a strong association (r=-0.79) between ICA score and the level of NfL in MS patients.\n\nConclusionsICA can be used as a digital biomarker for assessment and monitoring of cognitive performance in MS patients. In comparison to standard cognitive tools for MS (e.g. BICAMS), ICA is shorter in duration, does not show a learning bias, is independent of language, and takes advantage of artificial intelligence (AI) to identify cognitive status of patients more accurately. Being a digital test, it further has the potential for easier electronic health record or research database integration.

neuroscience↗

bioRxiv2018

Integrated Cognitive Assessment: Speed and Accuracy of Visual Processing as a Reliable Proxy to Cognitive Performance

Various mental disorders are accompanied by some degree of cognitive impairment. Particularly in neurodegenerative disorders, cognitive impairment is the phenotypical hallmark of the disease. Effective, accurate and timely cognitive assessment is key to early diagnosis of this family of mental disorders. Current standard-of-care techniques for cognitive assessment are primarily paper-based, and need to be administered by a healthcare professional; they are additionally language and education-dependent and typically suffer from a learning bias. These tests are thus not ideal for large-scale pro-active cognitive screening and disease progression monitoring. We developed the Integrated Cognitive Assessment (ICA), a 5-minute computerized cognitive assessment tool based on a rapid visual categorization task, in which a series of carefully selected natural images of varied difficulty are presented to participants. Overall 448 participants, across a wide age-range with different levels of education took the ICA test. We compared participants ICA test results with a variety of standard pen-and-paper tests that are routinely used to assess cognitive performance. ICA had excellent test-retest reliability, and was significantly correlated with all the reference cognitive tests used here, demonstrating ICAs ability as one unified test that can assess various cognitive domains.

neuroscience↗

bioRxiv2018

Beyond Core Object Recognition: Recurrent processes account for object recognition under occlusion

Core object recognition, the ability to rapidly recognize objects despite variations in their appearance, is largely solved through the feedforward processing of visual information. Deep neural networks are shown to achieve human-level performance in these tasks, and explain the primate brain representation. On the other hand, object recognition under more challenging conditions (i.e. beyond the core recognition problem) is less characterized. One such example is object recognition under occlusion. It is unclear to what extent feedforward and recurrent processes contribute in object recognition under occlusion. Furthermore, we do not know whether the conventional deep neural networks, such as AlexNet, which were shown to be successful in solving core object recognition, can perform similarly well in problems that go beyond the core recognition. Here, we characterize neural dynamics of object recognition under occlusion, using magnetoencephalography (MEG), while participants were presented with images of objects with various levels of occlusion. We provide evidence from multivariate analysis of MEG data, behavioral data, and computational modelling, demonstrating an essential role for recurrent processes in object recognition under occlusion. Furthermore, the computational model with local recurrent connections, used here, suggests a mechanistic explanation of how the human brain might be solving this problem. Author SummaryIn recent years, deep-learning-based computer vision algorithms have been able to achieve human-level performance in several object recognition tasks. This has also contributed in our understanding of how our brain may be solving these recognition tasks. However, object recognition under more challenging conditions, such as occlusion, is less characterized. Temporal dynamics of object recognition under occlusion is largely unknown in the human brain. Furthermore, we do not know if the previously successful deep-learning algorithms can similarly achieve human-level performance in these more challenging object recognition tasks. By linking brain data with behavior, and computational modeling, we characterized temporal dynamics of object recognition under occlusion, and proposed a computational mechanism that explains both behavioral and the neural data in humans. This provides a plausible mechanistic explanation for how our brain might be solving object recognition under more challenging conditions.

neuroscience↗