Summary Multiple sclerosis (MS) is a progressive disease of the CNS that is characterised by widespread lesions in the brain and spinal cord. MS results in motor, cognitive, and neuropsychiatric symptoms, all of which can occur independently of one another. The common cognitive symptoms include deficits in complex attention, efficiency of information processing, executive functioning, processing speed, and long-term memory. These deficits detrimentally affect many aspects of daily life, such as the ability to run a household, participate fully in society, and maintain employment—factors that can all affect the overall quality of life of the patient. The increased use of neuroimaging techniques in patients with MS has advanced our understanding of structural and functional changes in the brain that are characteristic of this disease, although much remains to be learned. Moreover, examination of efforts to treat the cognitive deficits in MS is still in the early stages.

https://www.thelancet.com/pdfs/journals/laneur/PIIS1474-4422(08)70259-X.pdf

Cognitive impairment in multiple sclerosis

https://www.thelancet.com/pdfs/journals/lancet/PIIS0140673604157942.pdf

Fatigue in neurological disorders

Fatigue and basal ganglia

Fatigue is a common disabling symptom of multiple sclerosis (MS). It is often considered a state of exhaustion distinct from depressed mood or physical weakness. Fatigue can be assessed by either self-report scales or performance-based measures; however, neither method captures all features of fatigue. Fatigue in MS frequently leads to unemployment. It is associated with a sense of loss of control over one's environment, low positive affect, psychological distress and neurological impairment. To date there is no reproducible neuroimaging marker or biological correlate that has been identified. Proposed pathological mechanisms of fatigue in MS include neuronal factors such as dysfunction of premotor, limbic, basal ganglia or hypothalamic areas; disruption of the neuroendrocrine axis leading to low arousal; alteration in serotoninergic pathways; changes in neurotransmitter levels; and altered CNS functioning caused by a disruption of the immune response. Treatment of fatigue is best approached in a multidisciplinary fashion that incorporates nonpharmacological interventions as well as medication. Amantadine and modafinil are among the most commonly used medications for fatigue associated with MS. Both medications have been studied with positive results in controlled clinical trials. Additional research towards measurement and pathogenesis of fatigue will hopefully lead to improved therapies.

/pdf/fatigue-in-multiple-sclerosis-37pna1ki8v.pdf

Fatigue in Multiple Sclerosis

Quality of life in patients with multiple sclerosis: the impact of fatigue and depression.

To investigate the pathophysiology of fatigue in MS, we assessed cerebral glucose metabolism (CMR-Glu) in 47 MS patients using PET and 18F-fluorodeoxyglucose. Applying the Fatigue Severity Scale (FSS), we first compared MS patients with severe fatigue (MS-FAT, n = 19, FSS > 4.9) and MS patients without fatigue (MS-NOF, n = 16, FSS < 3.7) on a pixel-by-pixel basis using Statistical Parametric Mapping (SPM95). Second, we compared FSS values of all 47 patients covering the whole range of this scale with CMRGlu using an analysis of covariance (SPM95). In addition, we determined global CMRGlu by region-of-interest analysis. Sixteen healthy subjects served as control subjects (CON). Global CMRGlu was significantly lower in both MS groups compared with CON (CON 43.3 +/- 6.9 mumol/100 mL/min, MS-FAT 34.7 +/- 4.4, MS-NOF 35.4 +/- 4.5) but was not related to fatigue severity. Comparing the two MS groups, SPM95 analysis revealed predominant CMRGlu reductions bilaterally in a prefrontal area involving the lateral and medial prefrontal cortex and adjacent white matter, in the premotor cortex, putamen, and in the right supplementary motor area of MS-FAT. In addition, there were CMRGlu reductions in the white matter extending from the rostral putamen toward the lateral head of the caudate nucleus. FSS values were inversely related to CMRGlu in the right prefrontal cortex. CMRGlu in the cerebellar vermis and anterior cingulate was relatively higher in MS-FAT than in MS-NOF patients. CMRGlu of both regions showed positive correlations with FSS values. Our data suggest that fatigue in MS is associated with frontal cortex and basal ganglia dysfunction that could result from demyelination of the frontal white matter.

Reduced glucose metabolism in the frontal cortex and basal ganglia of multiple sclerosis patients with fatigue: a 18F-fluorodeoxyglucose positron emission tomography study.

Reduced glucose metabolism in the frontal cortex and basal ganglia of multiple sclerosis patients with fatigue: A F-18-fluorodeoxyglucose positron emission tomography study

High breast density is a well-known risk factor for breast cancer. This study aimed to develop and adapt two (MLO, CC) deep convolutional neural networks (DCNN) for automatic breast density classification on synthetic 2D tomosynthesis reconstructions.In total, 4605 synthetic 2D images (1665 patients, age: 57 ± 37 years) were labeled according to the ACR (American College of Radiology) density (A-D). Two DCNNs with 11 convolutional layers and 3 fully connected layers each, were trained with 70% of the data, whereas 20% was used for validation. The remaining 10% were used as a separate test dataset with 460 images (380 patients). All mammograms in the test dataset were read blinded by two radiologists (reader 1 with two and reader 2 with 11 years of dedicated mammographic experience in breast imaging), and the consensus was formed as the reference standard. The inter- and intra-reader reliabilities were assessed by calculating Cohen's kappa coefficients, and diagnostic accuracy measures of automated classification were evaluated.The two models for MLO and CC projections had a mean sensitivity of 80.4% (95%-CI 72.2-86.9), a specificity of 89.3% (95%-CI 85.4-92.3), and an accuracy of 89.6% (95%-CI 88.1-90.9) in the differentiation between ACR A/B and ACR C/D. DCNN versus human and inter-reader agreement were both "substantial" (Cohen's kappa: 0.61 versus 0.63).The DCNN allows accurate, standardized, and observer-independent classification of breast density based on the ACR BI-RADS system.• A DCNN performs on par with human experts in breast density assessment for synthetic 2D tomosynthesis reconstructions. • The proposed technique may be useful for accurate, standardized, and observer-independent breast density evaluation of tomosynthesis. 

/pdf/diagnostic-accuracy-of-automated-acr-bi-rads-breast-density-t9r95uxf.pdf

S Dellas

Papers

Reduced glucose metabolism in the frontal cortex and basal ganglia of multiple sclerosis patients with fatigue: a 18F-fluorodeoxyglucose positron emission tomography study.

Reduced glucose metabolism in the frontal cortex and basal ganglia of multiple sclerosis patients with fatigue: A F-18-fluorodeoxyglucose positron emission tomography study

Diagnostic accuracy of automated ACR BI-RADS breast density classification using deep convolutional neural networks