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Article ; Online: A new approach to symmetric registration of longitudinal structural MRI of the human brain.

Ardekani, Babak A

2022 Volume 373, Page(s) 109563

Abstract: Background: This paper presents the Automatic Temporal Registration Algorithm (ATRA) for symmetric rigid-body registration of longitudinal T: New method: The notion of leave-one-out consistent (LOOC) landmarks with respect to a supervised landmark ... ...

Abstract	Background: This paper presents the Automatic Temporal Registration Algorithm (ATRA) for symmetric rigid-body registration of longitudinal T New method: The notion of leave-one-out consistent (LOOC) landmarks with respect to a supervised landmark detection algorithm is introduced. An automatic algorithm is presented for identification of LOOC landmarks on MRI scans. Multiple sets of LOOC landmarks are identified on each volume and a Generalized Orthogonal Procrustes Analysis of the landmarks is used to find a rigid-body transformation of each volume into a common space where the volumes are aligned precisely. Results: Qualitative and quantitative evaluations of ATRA registration accuracy were performed using 2012 volumes from 503 subjects (4 longitudinal volumes/subject), and on a further 120 volumes acquired from 3 normal subjects (40 longitudinal volumes/subject). Since the ground truth registrations are unknown, we devised a novel method for showing that ATRA's registration accuracy is at least better than 0.5 mm translation or 0.5° rotation. Comparison with existing method(s): In comparison with existing methods, ATRA does not require any image preprocessing (e.g., skull-stripping or intensity normalization) and can handle conditions where rigid-body motion assumptions are not true (e.g., movement in eyes, jaw, neck) and brain tissue loss over time in neurodegenerative diseases. In a systematic comparison with the FSL FLIRT algorithm, ATRA provided faster and more accurate registrations. Conclusions: The algorithm is symmetric, in the sense that any permutation of the input volumes does not change the transformation matrices, and unbiased, in that all volumes undergo exactly one interpolation operation, which precisely aligns them in a common space. There is no interpolation bias and no reference volume. All volumes are treated exactly the same. The algorithm is fast and highly accurate.
MeSH term(s)	Algorithms ; Brain/diagnostic imaging ; Head ; Humans ; Magnetic Resonance Imaging/methods ; Skull
Language	English
Publishing date	2022-03-11
Publishing country	Netherlands
Document type	Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	282721-9
ISSN	1872-678X ; 0165-0270
ISSN (online)	1872-678X
ISSN	0165-0270
DOI	10.1016/j.jneumeth.2022.109563
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Article: Prediction of brain age in individuals with and at risk for alcohol use disorder using brain morphological features.

Kamarajan, Chella / Ardekani, Babak A / Pandey, Ashwini K / Meyers, Jacquelyn L / Chorlian, David B / Kinreich, Sivan / Pandey, Gayathri / Richard, Christian / de Viteri, Stacey Saenz / Kuang, Weipeng / Porjesz, Bernice

bioRxiv : the preprint server for biology

2024

Abstract: Brain age measures predicted from structural and functional brain features are increasingly being used to understand brain integrity, disorders, and health. While there is a vast literature showing aberrations in both structural and functional brain ... ...

Abstract	Brain age measures predicted from structural and functional brain features are increasingly being used to understand brain integrity, disorders, and health. While there is a vast literature showing aberrations in both structural and functional brain measures in individuals with and at risk for alcohol use disorder (AUD), few studies have investigated brain age in these groups. The current study examines brain age measures predicted using brain morphological features, such as cortical thickness and brain volume, in individuals with a lifetime diagnosis of AUD as well as in those at higher risk to develop AUD from families with multiple members affected with AUD (i.e., higher family history density (FHD) scores). The AUD dataset included a group of 30 adult males (mean age = 41.25 years) with a lifetime diagnosis of AUD and currently abstinent and a group of 30 male controls (mean age = 27.24 years) without any history of AUD. A second dataset of young adults who were categorized based on their FHD scores comprised a group of 40 individuals (20 males) with high FHD of AUD (mean age = 25.33 years) and a group of 31 individuals (18 males) with low FHD (mean age = 25.47 years). Brain age was predicted using 187 brain morphological features of cortical thickness and brain volume in an XGBoost regression model; a bias-correction procedure was applied to the predicted brain age. Results showed that both AUD and high FHD individuals showed an increase of 1.70 and 0.09 years (1.08 months), respectively, in their brain age relative to their chronological age, suggesting accelerated brain aging in AUD and risk for AUD. Increased brain age was associated with poor performance on neurocognitive tests of executive functioning in both AUD and high FHD individuals, indicating that brain age can also serve as a proxy for cognitive functioning and brain health. These findings on brain aging in these groups may have important implications for the prevention and treatment of AUD and ensuing cognitive decline.
Language	English
Publishing date	2024-03-04
Publishing country	United States
Document type	Preprint
DOI	10.1101/2024.03.01.582844
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Article ; Online: Effects of sex, age, and apolipoprotein E genotype on hippocampal parenchymal fraction in cognitively normal older adults.

Ardekani, Babak A / Izadi, Neema O / Hadid, Somar A / Meftah, Amir M / Bachman, Alvin H

Psychiatry research. Neuroimaging

2020 Volume 301, Page(s) 111107

Abstract: Early detection of Alzheimer's disease (AD) is important for timely interventions and developing new treatments. Hippocampus atrophy is an early biomarker of AD. The hippocampal parenchymal fraction (HPF) is a promising measure of hippocampal structural ... ...

Abstract	Early detection of Alzheimer's disease (AD) is important for timely interventions and developing new treatments. Hippocampus atrophy is an early biomarker of AD. The hippocampal parenchymal fraction (HPF) is a promising measure of hippocampal structural integrity computed from structural MRI. It is important to characterize the dependence of HPF on covariates such as age and sex in the normal population to enhance its utility as a disease biomarker. We measured the HPF in 4239 structural MRI scans from 340 cognitively normal (CN) subjects aged 59-89 years from the AD Neuroimaging Initiative database, and studied its dependence on age, sex, apolipoprotein E (APOE) genotype, brain hemisphere, intracranial volume (ICV), and education using a linear mixed-effects model. In this CN cohort, HPF was inversely associated with ICV; was greater on the right hemisphere compared to left in both sexes with the degree of right > left asymmetry being slightly more pronounced in men; declined quadratically with age and faster in APOE ϵ4 carriers compared to non-carriers; and was significantly associated with cognitive ability. Consideration of HPF as an AD biomarker should be in conjunction with other subject attributes that are shown in this research to influence HPF levels in CN older individuals.
MeSH term(s)	Age Factors ; Aged ; Aged, 80 and over ; Apolipoproteins E/genetics ; Biomarkers/analysis ; Cognition ; Databases, Factual ; Female ; Genotype ; Hippocampus/anatomy & histology ; Humans ; Linear Models ; Male ; Middle Aged ; Neuroimaging/statistics & numerical data ; Organ Size ; Parenchymal Tissue/anatomy & histology ; Reference Values ; Sex Factors
Chemical Substances	ApoE protein, human ; Apolipoproteins E ; Biomarkers
Language	English
Publishing date	2020-05-14
Publishing country	Netherlands
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	445361-x
ISSN	1872-7506 ; 1872-7123 ; 0925-4927 ; 0165-1781
ISSN (online)	1872-7506 ; 1872-7123
ISSN	0925-4927 ; 0165-1781
DOI	10.1016/j.pscychresns.2020.111107
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Article ; Online: Survival Analysis in Cognitively Normal Subjects and in Patients with Mild Cognitive Impairment Using a Proportional Hazards Model with Extreme Gradient Boosting Regression.

Khajehpiri, Boshra / Moghaddam, Hamid Abrishami / Forouzanfar, Mohamad / Lashgari, Reza / Ramos-Cejudo, Jaime / Osorio, Ricardo S / Ardekani, Babak A

Journal of Alzheimer's disease : JAD

2021 Volume 85, Issue 2, Page(s) 837–850

Abstract: Background: Evaluating the risk of Alzheimer's disease (AD) in cognitively normal (CN) and patients with mild cognitive impairment (MCI) is extremely important. While MCI-to-AD progression risk has been studied extensively, few studies estimate CN-to- ... ...

Abstract	Background: Evaluating the risk of Alzheimer's disease (AD) in cognitively normal (CN) and patients with mild cognitive impairment (MCI) is extremely important. While MCI-to-AD progression risk has been studied extensively, few studies estimate CN-to-MCI conversion risk. The Cox proportional hazards (PH), a widely used survival analysis model, assumes a linear predictor-risk relationship. Generalizing the PH model to more complex predictor-risk relationships may increase risk estimation accuracy. Objective: The aim of this study was to develop a PH model using an Xgboost regressor, based on demographic, genetic, neuropsychiatric, and neuroimaging predictors to estimate risk of AD in patients with MCI, and the risk of MCI in CN subjects. Methods: We replaced the Cox PH linear model with an Xgboost regressor to capture complex interactions between predictors, and non-linear predictor-risk associations. We endeavored to limit model inputs to noninvasive and more widely available predictors in order to facilitate future applicability in a wider setting. Results: In MCI-to-AD (n = 882), the Xgboost model achieved a concordance index (C-index) of 84.5%. When the model was used for MCI risk prediction in CN (n = 100) individuals, the C-index was 73.3%. In both applications, the C-index was statistically significantly higher in the Xgboost in comparison to the Cox PH model. Conclusion: Using non-linear regressors such as Xgboost improves AD dementia risk assessment in CN and MCI. It is possible to achieve reasonable risk stratification using predictors that are relatively low-cost in terms of time, invasiveness, and availability. Future strategies for improving AD dementia risk estimation are discussed.
MeSH term(s)	Aged ; Aged, 80 and over ; Alzheimer Disease/diagnosis ; Alzheimer Disease/epidemiology ; Alzheimer Disease/genetics ; Cognitive Dysfunction/diagnosis ; Cognitive Dysfunction/epidemiology ; Cognitive Dysfunction/genetics ; Disease Progression ; Female ; Genetic Testing/methods ; Humans ; Magnetic Resonance Imaging ; Male ; Neuropsychological Tests ; Prognosis ; Proportional Hazards Models ; Risk Assessment/methods ; Survival Analysis
Language	English
Publishing date	2021-12-03
Publishing country	Netherlands
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	1440127-7
ISSN	1875-8908 ; 1387-2877
ISSN (online)	1875-8908
ISSN	1387-2877
DOI	10.3233/JAD-215266
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Article ; Online: Analysis of the MIRIAD Data Shows Sex Differences in Hippocampal Atrophy Progression.

Ardekani, Babak A / Convit, Antonio / Bachman, Alvin H

Journal of Alzheimer's disease : JAD

2016 Volume 50, Issue 3, Page(s) 847–857

Abstract: Background: Hippocampus (HC) atrophy is a hallmark of early Alzheimer's disease (AD). Atrophy rates can be measured by high-resolution structural MRI. Longitudinal studies have previously shown sex differences in the progression of functional and ... ...

Abstract	Background: Hippocampus (HC) atrophy is a hallmark of early Alzheimer's disease (AD). Atrophy rates can be measured by high-resolution structural MRI. Longitudinal studies have previously shown sex differences in the progression of functional and cognitive deficits and rates of brain atrophy in early AD dementia. It is important to corroborate these findings on independent datasets. Objective: To study temporal rates of HC atrophy over a one-year period in probable AD patients and cognitively normal (CN) subjects by longitudinal MRI scans obtained from the Minimal Interval Resonance Imaging in AD (MIRIAD) database. Methods: We used a novel algorithm to compute an index of hippocampal (volumetric) integrity (HI) at baseline and one-year follow-up in 43 mild-moderate probable AD patients and 22 CN subjects in MIRIAD. The diagnostic power of longitudinal HI measurement was assessed using a support vector machines (SVM) classifier. Results: The HI was significantly reduced in the AD group (p < 10(-20)). In addition, the annualized percentage rate of reduction in HI was significantly greater in the AD group (p < 10(-13)). Within the AD group, the annual reduction of HI in women was significantly greater than in men (p = 0.008). The accuracy of SVM classification between AD and CN subjects was estimated to be 97% by 10-fold cross-validation. Conclusion: In the MIRIAD patients with probable AD, the HC atrophies at a significantly faster rate in women as compared to men. Female sex is a risk factor for faster descent into AD. The HI measure has potential for AD diagnosis, as a biomarker of AD progression and a therapeutic target in clinical trials.
MeSH term(s)	Alzheimer Disease/complications ; Alzheimer Disease/pathology ; Atrophy/etiology ; Atrophy/pathology ; Disease Progression ; Female ; Hippocampus/pathology ; Humans ; Image Processing, Computer-Assisted ; Longitudinal Studies ; Magnetic Resonance Imaging ; Male
Language	English
Publishing date	2016
Publishing country	Netherlands
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	1440127-7
ISSN	1875-8908 ; 1387-2877
ISSN (online)	1875-8908
ISSN	1387-2877
DOI	10.3233/JAD-150780
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Article ; Online: Prediction of Incipient Alzheimer's Disease Dementia in Patients with Mild Cognitive Impairment.

Ardekani, Babak A / Bermudez, Elaine / Mubeen, Asim M / Bachman, Alvin H

Journal of Alzheimer's disease : JAD

2017 Volume 55, Issue 1, Page(s) 269–281

Abstract: Background: Mild cognitive impairment (MCI) is a transitional stage from normal aging to Alzheimer's disease (AD) dementia. It is extremely important to develop criteria that can be used to separate the MCI subjects at imminent risk of conversion to ... ...

Abstract	Background: Mild cognitive impairment (MCI) is a transitional stage from normal aging to Alzheimer's disease (AD) dementia. It is extremely important to develop criteria that can be used to separate the MCI subjects at imminent risk of conversion to Alzheimer-type dementia from those who would remain stable. We have developed an automatic algorithm for computing a novel measure of hippocampal volumetric integrity (HVI) from structural MRI scans that may be useful for this purpose. Objective: To determine the utility of HVI in classification between stable and progressive MCI patients using the Random Forest classification algorithm. Methods: We used a 16-dimensional feature space including bilateral HVI obtained from baseline and one-year follow-up structural MRI, cognitive tests, and genetic and demographic information to train a Random Forest classifier in a sample of 164 MCI subjects categorized into two groups [progressive (n = 86) or stable (n = 78)] based on future conversion (or lack thereof) of their diagnosis to probable AD. Results: The overall accuracy of classification was estimated to be 82.3% (86.0% sensitivity, 78.2% specificity). The accuracy in women (89.1%) was considerably higher than that in men (78.9%). The prediction accuracy achieved in women is the highest reported in any previous application of machine learning to AD diagnosis in MCI. Conclusion: The method presented in this paper can be used to separate stable MCI patients from those who are at early stages of AD dementia with high accuracy. There may be stronger indicators of imminent AD dementia in women with MCI as compared to men.
Language	English
Publishing date	2017
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	1440127-7
ISSN	1875-8908 ; 1387-2877
ISSN (online)	1875-8908
ISSN	1387-2877
DOI	10.3233/JAD-160594
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Article: Differentiating Individuals with and without Alcohol Use Disorder Using Resting-State fMRI Functional Connectivity of Reward Network, Neuropsychological Performance, and Impulsivity Measures.

Kamarajan, Chella / Ardekani, Babak A / Pandey, Ashwini K / Kinreich, Sivan / Pandey, Gayathri / Chorlian, David B / Meyers, Jacquelyn L / Zhang, Jian / Bermudez, Elaine / Kuang, Weipeng / Stimus, Arthur T / Porjesz, Bernice

Behavioral sciences (Basel, Switzerland)

2022 Volume 12, Issue 5

Abstract: Individuals with alcohol use disorder (AUD) may manifest an array of neural and behavioral abnormalities, including altered brain networks, impaired neurocognitive functioning, and heightened impulsivity. Using multidomain measures, the current study ... ...

Abstract	Individuals with alcohol use disorder (AUD) may manifest an array of neural and behavioral abnormalities, including altered brain networks, impaired neurocognitive functioning, and heightened impulsivity. Using multidomain measures, the current study aimed to identify specific features that can differentiate individuals with AUD from healthy controls (CTL), utilizing a random forests (RF) classification model. Features included fMRI-based resting-state functional connectivity (rsFC) across the reward network, neuropsychological task performance, and behavioral impulsivity scores, collected from thirty abstinent adult males with prior history of AUD and thirty CTL individuals without a history of AUD. It was found that the RF model achieved a classification accuracy of 86.67% (AUC = 93%) and identified key features of FC and impulsivity that significantly contributed to classifying AUD from CTL individuals. Impulsivity scores were the topmost predictors, followed by twelve rsFC features involving seventeen key reward regions in the brain, such as the ventral tegmental area, nucleus accumbens, anterior insula, anterior cingulate cortex, and other cortical and subcortical structures. Individuals with AUD manifested significant differences in impulsivity and alterations in functional connectivity relative to controls. Specifically, AUD showed heightened impulsivity and hypoconnectivity in nine connections across 13 regions and hyperconnectivity in three connections involving six regions. Relative to controls, visuo-spatial short-term working memory was also found to be impaired in AUD. In conclusion, specific multidomain features of brain connectivity, impulsivity, and neuropsychological performance can be used in a machine learning framework to effectively classify AUD individuals from healthy controls.
Language	English
Publishing date	2022-04-28
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2651997-5
ISSN	2076-328X
ISSN	2076-328X
DOI	10.3390/bs12050128
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Article ; Online: Platelet Function Is Associated With Dementia Risk in the Framingham Heart Study.

Ramos-Cejudo, Jaime / Johnson, Andrew D / Beiser, Alexa / Seshadri, Sudha / Salinas, Joel / Berger, Jeffrey S / Fillmore, Nathanael R / Do, Nhan / Zheng, Chunlei / Kovbasyuk, Zanetta / Ardekani, Babak A / Pomara, Nunzio / Bubu, Omonigho M / Parekh, Ankit / Convit, Antonio / Betensky, Rebecca A / Wisniewski, Thomas M / Osorio, Ricardo S

Journal of the American Heart Association

2022 Volume 11, Issue 9, Page(s) e023918

Abstract: Background Vascular function is compromised in Alzheimer disease (AD) years before amyloid and tau pathology are detected and a substantial body of work shows abnormal platelet activation states in patients with AD. The aim of our study was to ... ...

Abstract	Background Vascular function is compromised in Alzheimer disease (AD) years before amyloid and tau pathology are detected and a substantial body of work shows abnormal platelet activation states in patients with AD. The aim of our study was to investigate whether platelet function in middle age is independently associated with future risk of AD. Methods and Results We examined associations of baseline platelet function with incident dementia risk in the community-based FHS (Framingham Heart Study) longitudinal cohorts. The association between platelet function and risk of dementia was evaluated using the cumulative incidence function and inverse probability weighted Cox proportional cause-specific hazards regression models, with adjustment for demographic and clinical covariates. Platelet aggregation response was measured by light transmission aggregometry. The final study sample included 1847 FHS participants (average age, 53.0 years; 57.5% women). During follow-up (median, 20.5 years), we observed 154 cases of incident dementia, of which 121 were AD cases. Results from weighted models indicated that platelet aggregation response to adenosine diphosphate 1.0 µmol/L was independently and positively associated with dementia risk, and it was preceded in importance only by age and hypertension. Sensitivity analyses showed associations with the same directionality for participants defined as adenosine diphosphate hyper-responders, as well as the platelet response to 0.1 µmol/L epinephrine. Conclusions Our study shows individuals free of antiplatelet therapy with a higher platelet response are at higher risk of dementia in late life during a 20-year follow-up, reinforcing the role of platelet function in AD risk. This suggests that platelet phenotypes may be associated with the rate of dementia and potentially have prognostic value.
MeSH term(s)	Adenosine Diphosphate ; Alzheimer Disease/epidemiology ; Female ; Humans ; Longitudinal Studies ; Male ; Platelet Aggregation ; Platelet Function Tests ; Risk Factors
Chemical Substances	Adenosine Diphosphate (61D2G4IYVH)
Language	English
Publishing date	2022-04-26
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, N.I.H., Extramural
ZDB-ID	2653953-6
ISSN	2047-9980 ; 2047-9980
ISSN (online)	2047-9980
ISSN	2047-9980
DOI	10.1161/JAHA.121.023918
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Article ; Online: Hippocampal volumetric integrity in mesial temporal lobe epilepsy: A fast novel method for analysis of structural MRI.

Hakimi, Mathew / Ardekani, Babak A / Pressl, Christina / Blackmon, Karen / Thesen, Thomas / Devinsky, Orrin / Kuzniecky, Ruben I / Pardoe, Heath R

Epilepsy research

2019 Volume 154, Page(s) 157–162

Abstract: Objective: We investigate whether a rapid and novel automated MRI processing technique for assessing hippocampal volumetric integrity (HVI) can be used to identify hippocampal sclerosis (HS) in patients with mesial temporal lobe epilepsy (mTLE) and ... ...

Abstract	Objective: We investigate whether a rapid and novel automated MRI processing technique for assessing hippocampal volumetric integrity (HVI) can be used to identify hippocampal sclerosis (HS) in patients with mesial temporal lobe epilepsy (mTLE) and determine its performance relative to hippocampal volumetry (HV) and visual inspection. Methods: We applied the HVI technique to T1-weighted brain images from healthy control (n = 35), mTLE (n = 29), non-HS temporal lobe epilepsy (TLE, n = 44), and extratemporal focal epilepsy (EXTLE, n = 25) subjects imaged using a standardized epilepsy research imaging protocol and on non-standardized clinically acquired images from mTLE subjects (n = 40) to investigate if the technique is translatable to clinical practice. Performance of HVI, HV, and visual inspection was assessed using receiver operating characteristic (ROC) analysis. Results: mTLE patients from both research and clinical groups had significantly reduced ipsilateral HVI relative to controls (effect size: -0.053, 5.62%, p =  0.002 using a standardized research imaging protocol). For lateralizing mTLE, HVI had a sensitivity of 88% compared with a HV sensitivity of 92% when using specificity equal to 70%. Conclusions: The novel HVI approach can effectively detect HS in clinical populations, with an average image processing time of less than a minute. The fast processing speed suggests this technique could have utility as a quantitative tool to assist with imaging-based diagnosis and lateralization of HS in a clinical setting.
MeSH term(s)	Adult ; Epilepsy, Temporal Lobe/diagnostic imaging ; Female ; Hippocampus/diagnostic imaging ; Humans ; Image Processing, Computer-Assisted/methods ; Magnetic Resonance Imaging/methods ; Male ; Middle Aged ; Young Adult
Language	English
Publishing date	2019-05-24
Publishing country	Netherlands
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	632939-1
ISSN	1872-6844 ; 0920-1211
ISSN (online)	1872-6844
ISSN	0920-1211
DOI	10.1016/j.eplepsyres.2019.05.014
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Article ; Online: Model-based automatic detection of the anterior and posterior commissures on MRI scans.

Ardekani, Babak A / Bachman, Alvin H

NeuroImage

2009 Volume 46, Issue 3, Page(s) 677–682

Abstract: The projections of the anterior and posterior commissures (AC/PC) on the mid-sagittal plane of the human brain are important landmarks in neuroimaging. They can be used, for example, during MRI scanning for acquiring the imaging sections in a standard ... ...

Abstract	The projections of the anterior and posterior commissures (AC/PC) on the mid-sagittal plane of the human brain are important landmarks in neuroimaging. They can be used, for example, during MRI scanning for acquiring the imaging sections in a standard orientation. In post-acquisition image processing, these landmarks serve to establish an anatomically-based frame of reference within the brain that can be extremely useful in designing automated image analysis algorithms such as image segmentation and registration methods. This paper presents a fully automatic model-based algorithm for AC/PC detection on MRI scans. The algorithm utilizes information from a number of model images on which the locations of the AC/PC and a reference point (the vertex of the superior pontine sulcus) are known. This information is then used to locate the landmarks on test scans by template matching. The algorithm is designed to be fast, robust, and accurate. The method is flexible in that it can be trained to work on different image contrasts, optimized for different populations, or scanning modes. To assess the effectiveness of this technique, we compared automatically and manually detected landmark locations on 84 T(1)-weighted and 42 T(2)-weighted test scans. Overall, the average Euclidean distance between automatically and manually detected landmarks was 1.1 mm. A software implementation of the algorithm is freely available online at www.nitrc.org/projects/art.
MeSH term(s)	Algorithms ; Artificial Intelligence ; Cerebral Cortex/pathology ; Computer Simulation ; Humans ; Image Enhancement/methods ; Image Interpretation, Computer-Assisted/methods ; Imaging, Three-Dimensional/methods ; Magnetic Resonance Imaging/methods ; Models, Neurological ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; Schizophrenia/pathology ; Sensitivity and Specificity
Language	English
Publishing date	2009-03-03
Publishing country	United States
Document type	Evaluation Study ; Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	1147767-2
ISSN	1095-9572 ; 1053-8119
ISSN (online)	1095-9572
ISSN	1053-8119
DOI	10.1016/j.neuroimage.2009.02.030
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