Josep Daunis-i-Estadella

Wallerian Degeneration in the Corticospinal Tract Evaluated by Diffusion Tensor Imaging Correlates with Motor Deficit 30 Days after Middle Cerebral Artery Ischemic Stroke

BACKGROUND AND PURPOSE: The quantification and clinical significance of WD in CSTs following supratentorial stroke are not well understood. We evaluated the anisotropy by using DTI and signal-intensity changes on conventional MR imaging in the CST to determine whether these findings are correlated with limb motor deficit in patients with MCA ischemic stroke. MATERIALS AND METHODS: We studied 60 patients within 12 hours of stroke onset. At admission, day 3, and day 30 of evolution, patients underwent multimodal MR imaging, including DTI sequences. We assessed the severity of limb weakness by using the motor subindex scores (5a, 5b, 6a, 6b) of the m-NIHSS and established 3 groups: I (m-NIHSS scores of 0), II (m-NIHSS, 1–4), and III (m-NIHSS, 5–8). FA values and rFAs were measured on the affected and the unaffected CSTs in the pons. RESULTS: FA values for the CST were significantly lower on the affected side compared with the unaffected side only at day 30 (P < .001), and the rFA was significantly correlated with the motor deficit at day 30 (P < .001; r = −0.793). The sensitivity, specificity, and positive and negative predictive values for motor deficit by rFA < 0.925 were 95.2%, 94.9%, 90.9%, and 97.4%, respectively. CONCLUSIONS: WD in the CST revealed by DTI correlates with motor deficit 30 days after MCA ischemic stroke. This study highlights the utility of imaging follow-up at 30 days and the potential of DTI as a surrogate marker in clinical trials.

Acute damage to the posterior limb of the internal capsule on diffusion tensor tractography as an early imaging predictor of motor outcome after stroke.

Practical applications of diffusion tensor imaging are few, but this seems to be an interesting and a potentially important one: can it be used to predict motor outcome after stroke? Sixty patients within 12 hours of stroke were assessed with tractography at 5 different locations in the corticospinal tracts at admission, and at days 3 and 30. Patients with acute damage to the posterior limb of the internal capsule had the worst outcome and clinical severity at presentation. Conclusions: In the acute setting, tractography is promising for stroke mapping to predict motor outcome. Acute corticospinal tract damage at the level of the posterior limb of the internal capsule is a significant predictor of unfavorable motor outcome. BACKGROUND AND PURPOSE: Early prediction of motor outcome is of interest in stroke management. We aimed to determine whether lesion location at DTT is predictive of motor outcome after acute stroke and whether this information improves the predictive accuracy of the clinical scores. MATERIALS AND METHODS: We evaluated 60 consecutive patients within 12 hours of middle cerebral artery stroke onset. We used DTT to evaluate CST involvement in the motor cortex and premotor cortex, centrum semiovale, corona radiata, and PLIC and in combinations of these regions at admission, at day 3, and at day 30. Severity of limb weakness was assessed by using the motor subindex scores of the National Institutes of Health Stroke Scale (5a, 5b, 6a, 6b). We calculated volumes of infarct and fractional anisotropy values in the CST of the pons. RESULTS: Acute damage to the PLIC was the best predictor associated with poor motor outcome, axonal damage, and clinical severity at admission (P < .001). There was no significant correlation between acute infarct volume and motor outcome at day 90 (P = .176, r = 0.485). The sensitivity, specificity, and positive and negative predictive values of acute CST involvement at the level of the PLIC for motor outcome at day 90 were 73.7%, 100%, 100%, and 89.1%, respectively. In the acute stage, DTT predicted motor outcome at day 90 better than the clinical scores (R2 = 75.50, F = 80.09, P < .001). CONCLUSIONS: In the acute setting, DTT is promising for stroke mapping to predict motor outcome. Acute CST damage at the level of the PLIC is a significant predictor of unfavorable motor outcome.

Quantification of thrombus Hounsfield units on noncontrast CT predicts stroke subtype and early recanalization after intravenous recombinant tissue plasminogen activator

Anecdotally we know that high-density clots are probably more organized and difficult to lyse. These investigators calculated HU values for MCA thrombi on noncontrast CT within 4.5 hours of symptom onset and correlated it with successful recanalization after intravenous tPA treatment given 169 +/− 102 minutes thereafter. Best outcomes were achieved for M1, low-density, and thrombi not originating from the heart. Worse outcomes were related to high-density thrombi and those originating from the heart. BACKGROUND AND PURPOSE: Little is known about the factors that determine recanalization after intravenous thrombolysis. We assessed the value of thrombus Hounsfield unit quantification as a predictive marker of stroke subtype and MCA recanalization after intravenous rtPA treatment. MATERIALS AND METHODS: NCCT scans and CTA were performed on patients with MCA acute stroke within 4.5 hours of symptom onset. Demographics, stroke severity, vessel hyperattenuation, occlusion site, thrombus length, and time to thrombolysis were recorded. Stroke origin was categorized as LAA, cardioembolic, or indeterminate according to TOAST criteria. Two blinded neuroradiologists calculated the Hounsfield unit values for the thrombus and contralateral MCA segment. We used ROC curves to determine the rHU cutoff point to discriminate patients with successful recanalization from those without. We assessed the accuracy (sensitivity, specificity, and positive and negative predictive values) of rHU in the prediction of recanalization. RESULTS: Of 87 consecutive patients, 45 received intravenous rtPA and only 15 (33.3%) patients had acute recanalization. rHU values and stroke mechanism were the highest predictive factors of recanalization. The Matthews correlation coefficient was highest for rHU (0.901). The sensitivity, specificity, and positive and negative predictive values for lack of recanalization after intravenous rtPA for rHU ≤ 1.382 were 100%, 86.67%, 93.75%, and 100%, respectively. LAA thrombi had lower rHU than cardioembolic and indeterminate stroke thrombi (P = .004). CONCLUSIONS: The Hounsfield unit thrombus measurement ratio can predict recanalization with intravenous rtPA and may have clinical utility for endovascular treatment decision making.

Decreased Corticospinal Tract Fractional Anisotropy Predicts Long-term Motor Outcome After Stroke

Background and Purpose— Nearly 50% of patients have residual motor deficits after stroke, and long-term motor outcome is difficult to predict. We assessed the predictive value of axonal damage to the corticospinal tract indexed by diffusion tensor imaging fractional anisotropy for long-term motor outcome. Methods— Consecutive patients with middle cerebral artery stroke underwent multimodal MRI, including diffusion tensor imaging ⩽12 hours, 3 days, and 30 days after onset. Clinical severity, infarct volume, location of corticospinal tract damage on diffusion tensor tractography, and ratios of fractional anisotropy (rFA) between affected and unaffected sides of the corticospinal tract at the pons were evaluated. Severity of motor deficit at 2 years was categorized using the Motricity Index as no deficit (Motricity Index, 100), slight-moderate deficit (Motricity Index, 99–50), or severe deficit (Motricity Index, <50). Results— We evaluated 70 patients (28 women; 72±12 years). rFA values at day 30 correlated with the degree of motor deficit at 2 years (P<0.001). rFA at day 30 was the only independent predictor of long-term motor outcome (odds ratio, 1.60; 95% confidence interval, 1.26–2.03; P<0.001). The sensitivity, specificity, and positive and negative predictive values of the cutoffs rFA<0.982 for predicting slight-moderate deficit and rFA<0.689 for severe deficit were 94.4%, 84.6%, 73.9%, and 97.1%, respectively, and 100%, 83.3%, 81.3%, and 100%, respectively. Conclusions— rFA at day 30 is an independent predictor of long-term motor outcome after stroke.

Exploratory compositional data analysis

Abstract This paper presents the first steps that should be performed whenever the study of a compositional dataset is initiated. Centre, variation matrix and total variance of a compositional dataset are introduced. In addition the biplots are also introduced as a powerful tool to analyse and discover special features related to subcompositions. The exploratory methodology is applied to a dataset consisting of the major, minor and trace elements composition of soil samples from several places in Tuscany (Italy). The structure of the data, collected from three different known country rocks of ophiolitic nature (basic and ultrabasic rocks), represent an interesting case study for experimenting on new methodologies of statistical investigation and for pointing out differences related to parental chemistry and mineralogy as well as the nature of processes to be related to the subsequent evolution.

Gut Microbiota Interacts With Brain Microstructure and Function

CONTEXT Evidence from animals suggests that gut microbiota affects brain structure and function but evidence in humans is scarce. OBJECTIVE This study sought to evaluate potential interactions among gut microbiota composition, brain microstructure, and cognitive tests in obese and nonobese subjects. DESIGN, SETTING, AND PARTICIPANTS This was a cross-sectional study at a tertiary hospital including 20 consecutive obese and 19 nonobese subjects similar in age and sex. MAIN OUTCOME MEASURES Gut microbiota (16S bacterial gene pyrosequencing), brain microstructure (diffusion tensor imaging of brain white and gray matter and R2* sequences in magnetic resonance imaging) and cognitive tests. RESULTS Hierarchical clustering revealed a specific gut microbiota-brain map profile for obese individuals who could be discriminated from nonobese subjects (accuracy of 0.81). Strikingly, Shannon index was linked to R2* and fractional anisotropy of the hypothalamus, caudate nucleus, and hippocampus, suggesting sparing of these brain structures with increased bacterial biodiversity. Microbiota profile also clustered with cognitive function. The relative abundance of Actinobacteria phylum was linked not only to magnetic resonance imaging diffusion tensor imaging variables in the thalamus, hypothalamus, and amygdala but also to cognitive test scores related to speed, attention, and cognitive flexibility. CONCLUSIONS In sum, obesity status affects microbiota-brain microstructure and function crosstalk.

Brain Iron Overload, Insulin Resistance and Cognitive Performance in Obese Subjects: A Preliminary MRI Case-Control Study

OBJECTIVE The linkage among the tissue iron stores, insulin resistance (IR), and cognition remains unclear in the obese population. We aimed to identify the factors that contribute to increased hepatic iron concentration (HIC) and brain iron overload (BIO), as evaluated by MRI, and to evaluate their impact on cognitive performance in obese and nonobese subjects. RESEARCH DESIGN AND METHODS We prospectively recruited 23 middle-aged obese subjects without diabetes (13 women; age 50.4 ± 7.7 years; BMI 43.7 ± 4.48 kg/m2) and 20 healthy nonobese volunteers (10 women; age 48.8 ± 9.5 years; BMI 24.3 ± 3.54 kg/m2) in whom iron load was assessed in white and gray matter and the liver by MRI. IR was measured from HOMA-IR and an oral glucose tolerance test. A battery of neuropsychological tests was used to evaluate the cognitive performance. Multivariate regression analysis was used to identify the independent associations of BIO and cognitive performance. RESULTS A significant increase in iron load was detected at the caudate nucleus (P < 0.001), lenticular nucleus (P = 0.004), hypothalamus (P = 0.002), hippocampus (P < 0.001), and liver (P < 0.001) in obese subjects. There was a positive correlation between HIC and BIO at caudate (r = 0.517, P < 0.001), hypothalamus (r = 0.396, P = 0.009), and hippocampus (r = 0.347, P < 0.023). The area under the curve of insulin was independently associated with BIO at the caudate (P = 0.001), hippocampus (P = 0.028), and HIC (P = 0.025). BIOs at the caudate (P = 0.028), hypothalamus (P = 0.006), and lenticular nucleus (P = 0.012) were independently associated with worse cognitive performance. CONCLUSIONS Obesity and IR may contribute to increased HIC and BIO being associated with worse cognitive performance. BIO could be a potentially useful MRI biomarker for IR and obesity-associated cognitive dysfunction.

Reliability of the ABC/2 Method in Determining Acute Infarct Volume

Infarct volume is used as a surrogate outcome measure in clinical trials of therapies for acute ischemic stroke. ABC/2 is a fast volumetric method, but its accuracy remains to be determined. We aimed to study the accuracy and reproducibility of ABC/2 in determining acute infarct volume with diffusion‐weighted imaging.

Hypothalamic Damage Is Associated With Inflammatory Markers and Worse Cognitive Performance in Obese Subjects

CONTEXT Growing evidence implicates hypothalamic inflammation in the pathogenesis of diet-induced obesity and cognitive dysfunction in rodent models. Few studies have addressed the association between obesity and hypothalamic damage in humans and its relevance. OBJECTIVE This study aimed to determine markers of obesity-associated hypothalamic damage on diffusion tensor imaging (DTI) and to determine whether DTI metrics are associated with performance on cognitive testing. DESIGN AND PARTICIPANTS This cross-sectional study analyzed DTI metrics (primary [λ(1)], secondary [λ(2)], and tertiary [λ(3)] eigenvalues; fractional anisotropy; and mean diffusivity) in the hypothalamus of 24 consecutive middle-age obese subjects (13 women; 49.8 ± 8.1 y; body mass index [BMI], 43.9 ± 0.92 kg/m(2)) and 20 healthy volunteers (10 women; 48.8 ± 9.5 y; BMI, 24.3 ± 0.79 kg/m(2)). OUTCOME measures: Hypothalamic damage assessed by DTI metrics and cognitive performance evaluated by neuropsychological test battery. RESULTS λ(1) values in the hypothalamus were significantly lower in obese subjects (P < .0001). The sensitivity, specificity, and positive and negative predictive values for obesity-associated hypothalamic damage by λ(1) < 1.072 were 75, 87.5, 83.3, and 80.7%, respectively. Patients with hypothalamic λ(1) < 1.072 had higher values of BMI, fat mass, inflammatory markers, carotid-intima media thickness, and hepatic steatosis and lower scores on cognitive tests. Combined BMI and alanine aminotransferase had the strongest association with hypothalamic damage reflected by λ(1) < 1.072 (area under the curve = 0.89). CONCLUSIONS DTI detects obesity-associated hypothalamic damage associated with inflammatory markers and worse cognitive performance. This study highlights the potential utility of λ(1) as a surrogate marker of obesity-associated hypothalamic damage.

GRAFT, a complete system for data fusion

Data fusion concerns the problem of merging information coming from independent sources. Also known as statistical matching, file grafting or microdata merging, it is a challenging problem for statisticians. The increasing growth of collected data makes combining different sources of information an attractive alternative to single source data. The interest in data fusion derives, in certain cases, from the impossibility of attaining specific information from one source of data and the reduction of the cost entailed by this operation and, in all cases, from taking greater advantage of the available collected information. The GRAFT system is presented. It is a multipurpose data fusion system based on the k-nearest neighbor (k-nn) hot deck imputation method. The system aim is to cope with many data fusion problems and domains. The k-nn is a very demanding algorithm. The solutions envisaged and their cost, which allow this methodology to be used in a wide range of real problems, are presented.