TY  - JOUR
A1  - Haeussinger, Florian B.
A1  - Heinzel, Sebastian
A1  - Hahn, Tim
A1  - Schecklmann, Martin
A1  - Ehlis, Ann-Christine
A1  - Fallgatter, Andreas J.
T1  - Simulation of Near-Infrared Light Absorption Considering Individual Head and Prefrontal Cortex Anatomy: Implications for Optical Neuroimaging
JF  - PLoS ONE
N2  - Functional near-infrared spectroscopy (fNIRS) is an established optical neuroimaging method for measuring functional hemodynamic responses to infer neural activation. However, the impact of individual anatomy on the sensitivity of fNIRS measuring hemodynamics within cortical gray matter is still unknown. By means of Monte Carlo simulations and structural MRI of 23 healthy subjects (mean age: (25.0 +/- 2.8) years), we characterized the individual distribution of tissue-specific NIR-light absorption underneath 24 prefrontal fNIRS channels. We, thereby, investigated the impact of scalp-cortex distance (SCD), frontal sinus volume as well as sulcal morphology on gray matter volumes (V(gray)) traversed by NIR-light, i.e. anatomy-dependent fNIRS sensitivity. The NIR-light absorption between optodes was distributed describing a rotational ellipsoid with a mean penetration depth of (23.6 +/- 0.7) mm considering the deepest 5% of light. Of the detected photon packages scalp and bone absorbed (96.4 +/- 9: 7)% and V(gray) absorbed (3.1 +/- 1.8)% of the energy. The mean V(gray) volume (1.1 +/- 0.4)cm(3) was negatively correlated (r = - .76) with the SCD and frontal sinus volume (r = - .57) and was reduced by 41.5% in subjects with relatively large compared to small frontal sinus. Head circumference was significantly positively correlated with the mean SCD (r = .46) and the traversed frontal sinus volume (r = .43). Sulcal morphology had no significant impact on V(gray). Our findings suggest to consider individual SCD and frontal sinus volume as anatomical factors impacting fNIRS sensitivity. Head circumference may represent a practical measure to partly control for these sources of error variance.
KW  - Beer-lambert law
KW  - Adult head
KW  - Human brain
KW  - Spectroscopy fnirs
KW  - Photon migration
KW  - Propagation
KW  - Scattering
KW  - Model
KW  - Tissues
KW  - Media
Y1  - 2011
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-142311
VL  - 6
IS  - 10
ER  - 
TY  - JOUR
A1  - Mourão-Miranda, Janaina
A1  - Hardoon, David R.
A1  - Hahn, Tim
A1  - Marquand, Andre F.
A1  - Williams, Steve C.R.
A1  - Shawe-Taylor, John
A1  - Brammer, Michael
T1  - Patient classification as an outlier detection problem: An application of the One-Class Support Vector Machine
JF  - NeuroImage
N2  - Pattern recognition approaches, such as the Support Vector Machine (SVM), have been successfully used to classify groups of individuals based on their patterns of brain activity or structure. However these approaches focus on finding group differences and are not applicable to situations where one is interested in accessing deviations from a specific class or population. In the present work we propose an application of the one-class SVM (OC-SVM) to investigate if patterns of fMRI response to sad facial expressions in depressed patients would be classified as outliers in relation to patterns of healthy control subjects. We defined features based on whole brain voxels and anatomical regions. In both cases we found a significant correlation between the OC-SVM predictions and the patients' Hamilton Rating Scale for Depression (HRSD), i.e. the more depressed the patients were the more of an outlier they were. In addition the OC-SVM split the patient groups into two subgroups whose membership was associated with future response to treatment. When applied to region-based features the OC-SVM classified 52% of patients as outliers. However among the patients classified as outliers 70% did not respond to treatment and among those classified as non-outliers 89% responded to treatment. In addition 89% of the healthy controls were classified as non-outliers.
KW  - fMRI
KW  - Pattern classification
KW  - Depression
KW  - Machine learning
KW  - Support Vector Machine
KW  - Outlier detection
Y1  - 2011
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-141412
VL  - 58
IS  - 3
ER  -