Characterization and Diagnosis of Neurological and Neuropsychiatric Disorders
Ongoing since 2007
JD, Malin Björnsdotter, Srinivasan K, Francois B. Vialatte (ParisTech, EPSCI), Esteve Gallego (University of Vic), Jordi Casals (University of Vic), Moe Elgendi (now at University of Alberta), Jaeseung Jeong (KAIST), Andrzej Cichocki (RIKEN BSI, Japan)
We apply machine learning and signal processing to reveal brain patterns that characterize and predict neuropsychiatric and neurodegenerative disorders. We primarily study electro-encephalography (EEG) and magnetic resonance imaging (MRI) data from patients and control subjects, but also other parameters such as behavioral measures and genetics. We focus on Alzheimer’s disease and mild cognitive impairment (MCI), but we also study autism spectrum disorder (ASD) and attention deficit disorder (ADD). We ask questions such as: Can we detect if an individual suffers from Alzheimer’s disease based on a brain scan? Can we predict if (and, if so, when) a person will convert from healthy aging to mild cognitive impairment and to Alzheimer’s disease? Can we find brain-based biomarkers that accurately predict whether a child has genetic risk of developing autism?
We have examined the predictive value of EEG synchrony measures in distinguishing AD and MCI patients from age-matched control subjects. We have, for example, shown that Granger causality and stochastic event synchrony (SES) distinguishes MCI patients from controls at an accuracy of 83% (Figure 1; Dauwels et al., 2010). More recently, we have shown that random subsampling is an efficient and accurate method for detecting brain regions where the gray matter density (measured by MRI) is reduced in MCI and AD patients (Figure 2; Björnsdotter et al., 2013), and that this approach can incorporate other modalities (such as a behavioral parameters; Björnsdotter et al., 2012). This work may eventually lead to brain-based, reliable, multimodal diagnostic tools and in the development of biomarkers for neuropsychiatric, neurodevelopmental and neurodegenerative disorders.
Figure 1: Granger causality and stochastic event synchrony measures (SES)derived from EEG data effectively distinguished individuals with mild cognitive impairment from control subjects (Panel A). Panel B shows the electrode placement map for the 21 channel EEG system, and panel C shows an example of an EEG signal in the time-frequency domain used to compute the stochastic event synchrony measures.
Figure 2. A) Results on classification of individuals with Alzheimer's disease (AD, top panel) and mild cognitive impairment (MCI, bottom panel) vs. healthy, age-matched controls, showing that hippocampus gray matter is compromised in these conditions. From Björnsdotter et al., 2013. B) Results on simulated data showing that our algorithm for fusing a clinical score with local brain activity patterns outperforms either measure on its own both in terms of classifying patients vs. controls (Peak classification) and identifying brain regions that are impaired in the disorder (Mapping performance). From Björnsdotter et al., 2012.
C. Laske, H. R. Sohrabi, S. M. Frost, K. López-de-Ipiña, P. Garrard, M. Buscema, J. Dauwels, S. R. Soekadarl, S. Mueller, C. Linnemann, S. Bridenbaugh, Y. Kanagasingam, R. N. Martins, Sid E. O'Bryant, Innovative diagnostic tools for early detection of Alzheimer's disease, Alzheimer & Dementia, in press [ PDF ]
E. Gallego-Jutglà, J. Solé-Casals, F. Vialatte, J. Dauwels, A. Cichocki, EEG based index for early diagnosis of Alzheimer's disease, Journal of Alzheimer's Disease 43:3, 2015. [ PDF ]
E. Gallego-Jutglà, J. Solé-Casals, F. Vialatte, M. Elgendi, A. Cichocki, J. Dauwels, A hybrid feature selection approach for the early diagnosis of Alzheimer's disease, Journal of Neural Engineering, in press. [ PDF ]
Björnsdotter, M, Malmgren, H. and Dauwels, J. (2013) Volume-of-interest subsampling for diagnostic brain mapping: application to the ADNI cohort, 19th Annual Meeting of the Organization for Human Brain Mapping.
M. Björnsdotter, S. Rosenthal, D. Sona, J. Dauwels, Clustered subsampling for behaviorally informed diagnostic brain mapping, Fusion 2012 - 15th International Conference on Information Fusion, July 2012, Singapore [ PDF ].
J. Dauwels, Srinivasan K, Ramasubba Reddy M, T. Musha, F.Vialatte, C. Latchoumane, J. Jeong, A. Cichocki, Slowing and loss of complexity in Alzheimer's EEG: Two sides of the same coin?, International Journal of Alzheimer's Disease, (invited paper), Volume 2011 (2011), Article ID 539621, doi:10.4061/2011/539621 [ PDF ].
J. Dauwels, F. Vialatte, and A. Cichocki, Diagnosis of Alzheimer’s Disease from EEG Signals: Where Are We Standing?, Current Alzheimer's Research, 2010, 7(6):487-505 (invited paper) [ PDF ].
J. Dauwels, F. Vialatte, M. Vialatte, and A. Cichocki, A Comparative Study of Synchrony Measures for the Early Diagnosis of Alzheimer’s Disease Based on EEG, NeuroImage, 2010, 49:668-693 [ PDF ].
J. Dauwels, F. Vialatte, T.Weber, T.Musha and A. Cichocki, Quantifying Statistical Interdependence by Message Passing on Graphs PART II: Multi-Dimensional Point Processes, Neural Computation, 2009, 21(8):2203-2268 [ PDF ].
J. Dauwels, F. Vialatte, T. Rutkowski, and A. Cichocki, Measuring Neural Synchrony by Message Passing, In Advances in Neural Information Processing Systems 20, Edited by Platt J, Koller D, Singer Y, Roweis S, Cambridge, MA: MIT Press 2008:361-368
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E. Gallego-Jutglà, M. Elgendi, F. Vialatte, J. Solé-Casals, A. Cichocki, C. Latchoumane, J. Jeong, and J. Dauwels, Diagnosis of Alzheimer's Disease from EEG by Means of Synchrony Measures in Optimized Frequency Bands, EMBC 2012, San Diego, USA [ PDF ].
Localization of Seizure Focus from Interictal Intracranial EEG
Ongoing since 2008
JD, Jing Jin, Sydney Cash (MGH and Harvard Medical School), Andrew Cole (MGH and Harvard Medical School)
For approximately 30% of epilepsy patients, seizures are poorly controlled with medications alone. Those patients may be successfully treated by surgically removing the brain area(s) where the seizures originate; it is obviously crucial to accurately localize the seizure onset zone. To this end, one must often resort to semi-chronic invasive recordings of cortical activity, since non-invasive methods are frequently not conclusive. Currently neurologists rely heavily on seizures to determine the seizure focus. Since most patients have seizures infrequently, the invasive recordings must usually continue for days or weeks, until enough seizures are obtained; this procedure is costly, uncomfortable, and not without risk of side effects.
Our long-term objective is to drastically shorten the hospitalization of epilepsy patients, from several weeks to a few days: We hope to be able to determine the seizure focus from short invasive recordings in the operating room, made before resection of the seizure focus. The goal of the proposed project is to explore the feasibility of this idea, building upon our promising preliminary results.
Our hypothesis is that, even at rest, the seizure focus is characterized by seizure measures such as interictal spikes, high frequency oscillations (HFOs), slowing and synchrony. Our novelty is to exploit combinations of all measures in a machine-learning framework, to localize the seizure foci. We have proposed methods for measuring synchrony and detecting spikes and HFOs respectively.
As a first step, we have applied signal processing techniques to invasive semi-chronic recordings between seizures, in order to extract signatures of the seizure focus, such as slowing and locally increased synchrony, spikes and HFOs. We have designed statistical decision algorithms that leverage those different signatures to determine the seizure focus in an automated fashion.
Figure: Semichronic EEG recording containing epileptic seizures
In future work, we will apply those algorithms to short invasive recordings made in the operating room. We will compare our results with the gold standard, determined by clinicians from invasive recordings during seizures.
The proposed procedure may have enormous impact on clinical practice of epilepsy in Singapore and elsewhere, and would substantially reduce treatment costs. Moreover, our novel automated approach to medical decision making is not only relevant for neurosurgery but many other medical disciplines.
J. Dauwels, E. Eskandar, A. Cole, D. Hoch, R. Zepeda, Sydney S. Cash. Graphical Models for Localization of the Seizure Focus from Interictal Intracranial EEG. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2011)
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J. Dauwels, S. Cash, and E. Eskandar, Localization of Seizure Onset Area from Intracranial Non-Seizure EEG by Exploiting Locally Enhanced Synchrony, Proc. 31th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC09), in press. [ PDF ]
Jin, J., Dauwels, J., & Cash, S., Automated localization of the seizure focus using interictal intracranial EEG, Proc. 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2014), pp. 4439-4442). [ PDF ]
Figure: (a) Signatures of epileptic EEG activity: spikes (top) ands HFO (bottom); (b) physical locations of surface electrodes, where the actual focus is red-color labeled by doctors from ictal EEG; (c) the distribution of a measure (e.g. spike rate) over all electrodes, where the corresponding ground truth is marked using back-color squares.
Computational Modeling of Epileptic Seizures
Ongoing since 2012
P. N. Taylor, G. Baier (University of Manchester), S. S. Cash (MGH and Harvard Medical School), JD, J.J. Slotine (MIT), Yujiang Wang (University of Manchester)
Pathological brain rhythms such as those observed on the EEG of epileptic patients during a seizure are challenging to understand, and the seizures are difficult to treat clinically. Hypothesis driven, patient-specific treatments are desirable and computational modeling can represent an important step towards achieving this goal.
In our work we have developed models of epileptic brain activity which can reproduce many of the phenomena observed clinically in the spatial, temporal, and frequency domains. We have used our models to investigate hypotheses regarding stimulation protocols and to make explicit predictions with regard to heterogeneous brain activity.
Figure: Computational model of stimulus induced epileptic spike-wave discharges. (Left) Connectivity scheme of the model. (Right) Upper panel: time series of the cortical output containing a seizure, and time-frequency analysis using a Morlet wavelet transform. Lower panel: time series of a clinical recording of an epileptic seizure, and time-frequency analysis using a Morlet wavelet transform.
P. Taylor, Y. Wang, M. Goodfellow, J. Dauwels, F. Moeller, U. Stephani, G. Baier, A computational study of stimulus driven epileptic seizure abatement, PLOS One, Dec 2014, DOI: 10.1371/journal.pone.0114316. [ PDF ].
P. N. Taylor, G. Baier, S. S. Cash, J. Dauwels, J.J. Slotine, Yujiang Wang, A model of stimulus induced epileptic spike-wave discharges, SCCI 2013, 2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, accepted. [ PDF ].
Taylor P.N., Kaiser M., Dauwels J., Structural connectivity based whole brain modelling in epilepsy, Journal of Neuroscience Methods, 236, 51-57, 2014 (invited paper). [ PDF ].
Baier, G., Goodfellow, M., Taylor, P.N., Wang, Y., Garry, D.J., 2012, The importance of modelling epileptic seizure dynamics as spatio-temporal patterns, Frontiers in Physiology.
Taylor, P.N., Goodfellow, M., Wang, Y., Baier G., 2012, Towards a large scale model of epileptic spike-wave discharges, Biological Cybernetics.
Goodfellow, M., Taylor, P.N., Wang, Y., Garry, D.J., Baier, G., 2012. Modelling the role of tissue heterogeneity in epileptic rhythms, European Journal of Neuroscience.
Wang, Y., Goodfellow, M., Taylor, P.N., Baier, G., 2012, A minimal phase space approach to model prototypic epileptic dynamics, Physical Review E.
Taylor, P.N., Baier, G., 2011. A spatially extended model for macroscopic spike- wave discharges, Journal of Computational Neuroscience.