Voxel-Based Investigations of Regional Cerebral Blood Flow Abnormalities in Alzheimers Disease Using a Single-Detector Spect System

Duran, Fabio; Zampieri, Fernando G.; Bottino, Cássio M. C.; Buchpiguel, Carlos Alberto; Busatto, Geraldo F.

doi:10.1590/s1807-59322007000400002

Cited by 12 publications

(8 citation statements)

References 19 publications

(25 reference statements)

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“…Other recent SVM investigations of FDG-PET data have reported similar findings, with the somatosensory cortex among the brain regions showing highest discrimination between MCI patients converting to AD and healthy controls ( Pagani et al, 2015 ). Apparent functional hyperactivity in brain regions known to be spared until late stages of AD has been reported in previous PET and SPECT studies that performed mean group comparisons against elderly controls ( Duran et al, 2007 , Pagani et al, 2015 , Soonawala et al, 2002 ); since the metabolic activity and cerebral blood flow in AD individuals is decreased in many GM areas, data normalization of count values in each ROI to the global tracer uptake in the brain leads to an overestimation of the relative activity measures in AD subjects in the selected brain regions that are most notably spared by the neurodegenerative AD process ( Duran et al, 2007 ). The findings reported herein demonstrate that the classification of voxels with increased values in AD patients relative to healthy controls make an important contribution to their diagnostic discrimination when SVM-based methods are applied to PET, SPECT or T1-MRI data.…”

Section: Discussionmentioning

confidence: 79%

Selecting the most relevant brain regions to discriminate Alzheimer's disease patients from healthy controls using multiple kernel learning: A comparison across functional and structural imaging modalities and atlases

Rondina

Ferreira

Duran

et al. 2018

NeuroImage: Clinical

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BackgroundMachine learning techniques such as support vector machine (SVM) have been applied recently in order to accurately classify individuals with neuropsychiatric disorders such as Alzheimer's disease (AD) based on neuroimaging data. However, the multivariate nature of the SVM approach often precludes the identification of the brain regions that contribute most to classification accuracy. Multiple kernel learning (MKL) is a sparse machine learning method that allows the identification of the most relevant sources for the classification. By parcelating the brain into regions of interest (ROI) it is possible to use each ROI as a source to MKL (ROI-MKL).MethodsWe applied MKL to multimodal neuroimaging data in order to: 1) compare the diagnostic performance of ROI-MKL and whole-brain SVM in discriminating patients with AD from demographically matched healthy controls and 2) identify the most relevant brain regions to the classification. We used two atlases (AAL and Brodmann's) to parcelate the brain into ROIs and applied ROI-MKL to structural (T1) MRI, 18F-FDG-PET and regional cerebral blood flow SPECT (rCBF-SPECT) data acquired from the same subjects (20 patients with early AD and 18 controls). In ROI-MKL, each ROI received a weight (ROI-weight) that indicated the region's relevance to the classification. For each ROI, we also calculated whether there was a predominance of voxels indicating decreased or increased regional activity (for 18F-FDG-PET and rCBF-SPECT) or volume (for T1-MRI) in AD patients.ResultsCompared to whole-brain SVM, the ROI-MKL approach resulted in better accuracies (with either atlas) for classification using 18F-FDG-PET (92.5% accuracy for ROI-MKL versus 84% for whole-brain), but not when using rCBF-SPECT or T1-MRI. Although several cortical and subcortical regions contributed to discrimination, high ROI-weights and predominance of hypometabolism and atrophy were identified specially in medial parietal and temporo-limbic cortical regions. Also, the weight of discrimination due to a pattern of increased voxel-weight values in AD individuals was surprisingly high (ranging from approximately 20% to 40% depending on the imaging modality), located mainly in primary sensorimotor and visual cortices and subcortical nuclei.ConclusionThe MKL-ROI approach highlights the high discriminative weight of a subset of brain regions of known relevance to AD, the selection of which contributes to increased classification accuracy when applied to 18F-FDG-PET data. Moreover, the MKL-ROI approach demonstrates that brain regions typically spared in mild stages of AD also contribute substantially in the individual discrimination of AD patients from controls.

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Section: Discussionmentioning

confidence: 79%

Selecting the most relevant brain regions to discriminate Alzheimer's disease patients from healthy controls using multiple kernel learning: A comparison across functional and structural imaging modalities and atlases

Rondina

Ferreira

Duran

et al. 2018

NeuroImage: Clinical

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“…Specificity can be further increased by adding information from laboratory and neuroimaging tests, both for the purpose of ruling out comorbidities that concur with a high prevalence of cognitive impairment30 and to ascertain underlying pathological features of AD31. The complexity of this procedure is a limitation for the large-scale diagnosis of MCI, particularly in primary care settings.…”

Section: Discussionmentioning

confidence: 99%

Combining Cognitive Screening Tests for the Evaluation of Mild Cognitive Impairment in the Elderly

Ladeira¹,

Diniz²,

Nunes³

et al. 2009

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OBJECTIVETo determine the accuracy of the Mini-Mental State Examination combined with the Verbal Fluency Test and Clock Drawing Test for the identification of patients with mild cognitive impairment and Alzheimer’s disease (AD).METHODThese tests were used to evaluate cognitive function in 247 older adults. Subjects were divided into three groups according to their cognitive state: mild cognitive impairment (n=83), AD (n=81), cognitively unimpaired controls (n=83), based on clinical and neuropsychological data. The diagnostic accuracy of each test for discriminating between these diagnostic groups (mild cognitive impairment or AD vs. controls) was examined with the aid of Receiver Operating Characteristic (ROC) curves. Additionally, we evaluated the benefit of the combination of tests on diagnostic accuracy.RESULTSAlthough they were accurate enough for the identification of Alzheimer’s disease, neither test alone proved adequate for the correct separation of patients with mild cognitive impairment from healthy subjects. Combining these tests did not improve diagnostic accuracy, as compared to the Mini-Mental State Examination alone, in the identification of patients with mild cognitive impairment or Alzheimer’s disease.CONCLUSIONSThe present data do not warrant the combined use of the Mini-Mental State Examination, the Verbal Fluency Test and the Clock Drawing Test as a sufficient diagnostic schedule in screening for mild cognitive impairment. The present data do not support the notion that the combination of test scores is better that the use of Mini-Mental State Examination scores alone in the screening for Alzheimer’s disease.

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“…That pattern of hypometabolism has been detected even in very early stages of AD (Fig 5) and in subjects at genetic risk for the disorder (Duran et al, 2007). As AD progresses, the frontal association cortex becomes involved, while cerebellum, striatum, basal ganglia, primary visual, and sensorial and motor cortex remain unaltered (Mosconi, 2005).…”

Section: E T E C T E D E I T H E R B Y V I S U a L I N S P E C T I O mentioning

confidence: 78%

Currently Available Neuroimaging Approaches in Alzheimer Disease (AD) Early Diagnosis

Ortiz-Terán¹,

Santos²,

Martín³

et al. 2011

The Clinical Spectrum of Alzheimer's Disease -the Charge Toward Comprehensive Diagnostic and Therapeutic Strategies

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Computerized Tomography (CT)Nowadays CT is not considered a standard technique for diagnosing AD, least of all in early stages. Its main usefulness is in differential diagnosis since it is less expensive, faster and more widely available than MRI. CT allows ruling out some clinical situations which lead to MEGMeasures magnetic fields and, indirectly, yields rather precise information about brain electrical activity CT = computerized tomography; MRI = magnetic resonance imaging; fMRI = functional magnetic resonance imaging; DTI = diffusion tensor imaging; SPECT = single-photon emission computed tomography; PET = positron emission tomography; MEG = magnetoencephalography; BOLD = blood oxygen level-dependent; NAA = N-acetyl aspartate; Cr = Creatinine; AD = Alzheimer's disease Table 1. Typical findings of different brain imaging methods used in AD and MCI diagnosis Magnetic Resonance Imaging (MRI) 1.2.1 Structural MRIThis technique provides the best possible spatial resolution. Given the fact the most robust neuroimaging finding in AD is the atrophy of mesial temporal structures, structural MRI is the most widely used approach. Hippocampal atrophy is another well known finding, but atrophy within other brain areas has also been described in entorrhinal cortex, amygdala, basal ganglia (nucleus basalis of Meynert), thalamus and bilateral parietal cortex. It is relevant to mention here that in terms of early diagnosis some consider the entorrhinal cortex as the earliest area affected by AD. Other researchers have stated that the nucleus basalis of Meynert is a key structure in AD early diagnosis (Herholz et al, 2004;Grothe et al, 2010). Functional MRI (fMRI)fMRI provides signal intensities images associated with a relative cerebral blood flow during cognitive tasks. Resting and activation functional MRI studies have showed a lesser coordinated activity in the hippocampus, inferior parietal lobes -both bilaterally-and cingulate cortex in patients with AD. This neuroimaging technique is nowadays used to monitor AD patients' treatment. Combining genetic risk with functional MRI memory task paradigms has shown to be a very accurate pre-symptomatic predictor of cognitive decline. Diffusion Tensor Imaging (DTI)DTI allows visualizing neuronal connectivity or, more precisely, local fiber orientation and white matter tracts integrity. Patients with AD show decreased fiber density in temporal white matter, probably related to the medial temporal grey matter atrophy, as well as in the splenium of the corpus callosum. www.intechopen.com Currently Available Neuroimaging Approaches in Alzheimer Disease (AD) Early Diagnosis 149In mild cognitive impairment (MCI), however, this decreased fiber density has been located in the anterior part of the corpus callosum. SpectroscopyIt provides information on tissue substrate or metabolite concentrations. Specifically the Nacetyl aspartate (NAA) has been used as a biomarker of neuronal death, and it has been demonstrated that in patients with MCI and AD is significantly reduced compared to hea...

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Voxel-Based Investigations of Regional Cerebral Blood Flow Abnormalities in Alzheimers Disease Using a Single-Detector Spect System

Cited by 12 publications

References 19 publications

Selecting the most relevant brain regions to discriminate Alzheimer's disease patients from healthy controls using multiple kernel learning: A comparison across functional and structural imaging modalities and atlases

Selecting the most relevant brain regions to discriminate Alzheimer's disease patients from healthy controls using multiple kernel learning: A comparison across functional and structural imaging modalities and atlases

Combining Cognitive Screening Tests for the Evaluation of Mild Cognitive Impairment in the Elderly

Currently Available Neuroimaging Approaches in Alzheimer Disease (AD) Early Diagnosis

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