Classifying spatial patterns of brain activity with machine learning methods: Application to lie detection

Davatzikos, Christos; Ruparel, Kosha; Fan, Yong; Shen, Dinggang; Acharyya, Mausumi; Loughead, James; Gur, Ruben C.; Langleben, Daniel D.

doi:10.1016/j.neuroimage.2005.08.009

Cited by 381 publications

(266 citation statements)

References 22 publications

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“…Therefore, detection of this structural pattern can lead to very early diagnosis of prodromal AD. This study adds to mounting evidence in the literature for the importance of pattern classification methods in detecting subtle and complex structural and functional patterns [10,11,21,37].…”

Section: Introductionmentioning

confidence: 72%

Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging

Davatzikos

Fan

et al. 2008

Neurobiology of Aging

351

237

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We report evidence that computer-based high-dimensional pattern classification of MRI detects patterns of brain structure characterizing mild cognitive impairment (MCI), often a prodromal phase of Alzheimer's Disease (AD). 90% diagnostic accuracy was achieved, using cross-validation, for 30 participants in the Baltimore Longitudinal Study of Aging. Retrospective evaluation of serial scans obtained during prior years revealed gradual increases in structural abnormality for the MCI group, often before clinical symptoms, but slower increase for individuals remaining cognitively normal. Detecting complex patterns of brain abnormality in very early stages of cognitive impairment has pivotal importance for the detection and management of AD.

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Section: Introductionmentioning

confidence: 72%

Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging

Davatzikos

Fan

et al. 2008

Neurobiology of Aging

351

237

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“…We consider the specific choice of classification algorithm to be a modular aspect of our software design, but chose SVM for several reasons. First, SVM has been prevalent in the recent brain-state fMRI literature [Cox and Savoy, 2003;Davatzikos et al, 2005;LaConte et al, 2005c;Mitchell et al, 2004]. In addition, our comparison of SVM with linear discriminant analysis [LaConte et al, 2005c] indicated that SVMs tend to be less sensitive to preprocessing issues, which is highly desirable for realtime applications.…”

Section: Real-time Implementationmentioning

confidence: 91%

“…Consequently, there has been a remarkable surge in cognitive neuroscientific interest and inventive experimental designs focused on classification of brain states from fMRI data. The applications have been broad and include lie detection [Davatzikos et al, 2005], unconsciously perceived sensory stimuli [Haynes and Rees, 2005], behavioral choices in the context of emotional perception [Pessoa and Padmala, 2005], early visual areas [Kamitani and Tong, 2005], information-based mapping [Kriegeskorte et al, 2006], and memory recall [Polyn et al, 2005].…”

Section: Introductionmentioning

confidence: 99%

Real‐time fMRI using brain‐state classification

LaConte¹,

Peltier²,

Hu³

2006

Human Brain Mapping

226

187

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Abstract:We have implemented a real-time functional magnetic resonance imaging system based on multivariate classification. This approach is distinctly different from spatially localized real-time implementations, since it does not require prior assumptions about functional localization and individual performance strategies, and has the ability to provide feedback based on intuitive translations of brain state rather than localized fluctuations. Thus this approach provides the capability for a new class of experimental designs in which real-time feedback control of the stimulus is possible-rather than using a fixed paradigm, experiments can adaptively evolve as subjects receive brain-state feedback. In this report, we describe our implementation and characterize its performance capabilities. We observed $80% classification accuracy using whole brain, block-design, motor data. Within both left and right motor task conditions, important differences exist between the initial transient period produced by task switching (changing between rapid left or right index finger button presses) and the subsequent stable period during sustained activity. Further analysis revealed that very high accuracy is achievable during stable task periods, and that the responsiveness of the classifier to changes in task condition can be much faster than signal time-to-peak rates. Finally, we demonstrate the versatility of this implementation with respect to behavioral task, suggesting that our results are applicable across a spectrum of cognitive domains. Beyond basic research, this technology can complement electroencephalography-based brain computer interface research, and has potential applications in the areas of biofeedback rehabilitation, lie detection, learning studies, virtual reality-based training, and enhanced conscious awareness.

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“…However, VBM analyses are of limited value for individual diagnosis, since they measure group differences and are not equipped to classify individuals. Towards this goal, high-dimensional pattern classification methods have been pursued in recent years (Davatzikos, 2004;Duchesne et al, 2006;Fan et al, 2006a;Fan et al, 2007a;Fan et al, 2005Fan et al, , 2006bFan et al, 2007b;Lao et al, 2004;Liu et al, 2004;Timoner et al, 2002), and have shown great potential in a variety of neuroimaging studies (Davatzikos et al, in press, 2006;Davatzikos et al, 2005a;Davatzikos et al, 2005b;Fan et al, 2008a;Fan et al, 2005Fan et al, , 2006bFan et al, 2007b;Kawasaki et al, 2007;Mourao-Miranda et al, 2005;Yoon et al, 2007). Unlike VBM-type methods, which are mass univariate and don't consider statistical associations among different brain regions, high-dimensional pattern classification methods are multivariate, thereby leading to better group separation, which is critical for individual diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Structural and functional biomarkers of prodromal Alzheimer's disease: A high-dimensional pattern classification study

et al. 2008

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This work builds upon previous studies that reported high sensitivity and specificity in classifying individuals with mild cognitive impairment (MCI), which is often a prodromal phase of Alzheimer's Disease (AD), via pattern classification of MRI scans. The current study integrates MRI and PET 15 O water scans from 30 participants in the Baltimore Longitudinal Study of Aging, and tests the hypothesis that joint evaluation of structure and function can yield higher classification accuracy than either alone. Classification rates of up to 100% accuracy were achieved via leave-one-out cross validation, whereas conservative estimates of generalization performance in new scans, evaluated via bagging cross-validation, yielded an area under the receiver operating characteristic (ROC) curve equal to 0.978 (97.8%), indicating excellent diagnostic accuracy. Spatial maps of regions determined to contribute the most to the classification implicated many temporal, prefrontal, orbitofrontal, and parietal regions. Detecting complex patterns of brain abnormality in early stages of cognitive impairment has pivotal importance for the detection and management of AD. KeywordsAlzheimer's Disease; MCI; high-dimensional Pattern Classification; MRI; PET; voxel-based analysis; diagnosis of AD Amnestic mild cognitive impairment (MCI) is often a prodromal stage to Alzheimer's Disease (AD), as individuals with MCI may convert to AD at an annual rate as high as 15% . Therefore, MCI is frequently considered to be a good target for early AD diagnosis, and for therapeutic interventions. MRI and PET imaging can potentially be used as diagnostic tools that assess brain structure and function in a direct and objective way and have potential utility for diagnosis, prognosis, and evaluation of disease progression and treatment effects.Many imaging studies have found loss of grey matter (GM) and metabolic abnormalities in MCI. Most of the earlier studies were based on volumetric measurements of regions of interest (ROI's) (Chetelat et al., 2002;Convit et al., 2000;Dickerson et al., 2001;Fox et al., 1996a;Kaye et al., 1997;Killiany et al., 2000), such as the hippocampus and the entorhinal cortex, and have confirmed GM atrophy in regions that are known to be affected by AD. However, the pattern of AD pathology is complex and evolves as the disease progresses, starting mainly in the hippocampus and entorhinal cortex, and subsequently spreading throughout temporal and orbitofrontal cortex, posterior cingulate, and association cortex generally (Braak et al., 1998). Therefore, measuring volumes or average PET signals of a few structures cannot capture Voxel-based morphometry (VBM) has been proposed by a number of investigators as a more comprehensive way of measuring the spatial distribution of brain atrophy in MCI and AD, by evaluating images region by region instead of making a priori assumptions about specific ROIs. A variety of VBM-type methods exist (Ashburner and Friston, 2000;Chung et al., 2001;Davatzikos et al., 2001;Davatzikos et al., 1996...

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Classifying spatial patterns of brain activity with machine learning methods: Application to lie detection

Cited by 381 publications

References 22 publications

Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging

Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging

Real‐time fMRI using brain‐state classification

Structural and functional biomarkers of prodromal Alzheimer's disease: A high-dimensional pattern classification study

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