Using functional magnetic resonance imaging (fMRI), we mapped brain activity in six normal volunteers during two silent verbal fluency tasks, one with a phonemic (letter) cue and one with a semantic (category) cue. In comparison with resting state, both tasks activated the anterior triangular portion of the left inferior frontal gyrus (IFG or F3, for third frontal gyrus) and the left thalamus. There were also areas activated in one task but not in the other: the posterior opercular portion of the left IFG for phonemic fluency, and the left retrosplenial region for semantic fluency. Our findings concur with normal psychophysical data and neuropsychological observations to suggest the recruitment of two overlapping but dissociable systems for the two tasks, and demonstrate functional heterogeneity within the left IFG (Broca's area), where the opercular portion is responsible for obtaining access to words through a phonemic/articulatory route.

Determination of sensitive and specific markers of very early AD progression is intended to aid researchers and clinicians to develop new treatments and monitor their effectiveness, as well as to lessen the time and cost of clinical trials. Magnetic Resonance (MR)-related biomarkers have been recently identified by the use of machine learning methods for the in vivo differential diagnosis of AD. However, the vast majority of neuroimaging papers investigating this topic are focused on the difference between AD and patients with mild cognitive impairment (MCI), not considering the impact of MCI patients who will (MCIc) or not convert (MCInc) to AD. Morphological T1-weighted MRIs of 137 AD, 76 MCIc, 134 MCInc, and 162 healthy controls (CN) selected from the Alzheimer's disease neuroimaging initiative (ADNI) cohort, were used by an optimized machine learning algorithm. Voxels influencing the classification between these AD-related pre-clinical phases involved hippocampus, entorhinal cortex, basal ganglia, gyrus rectus, precuneus, and cerebellum, all critical regions known to be strongly involved in the pathophysiological mechanisms of AD. Classification accuracy was 76% AD vs. CN, 72% MCIc vs. CN, 66% MCIc vs. MCInc (nested 20-fold cross validation). Our data encourage the application of computer-based diagnosis in clinical practice of AD opening new prospective in the early management of AD patients.

Prostate cancer is the most common malignant tumors in men but prostate Magnetic Resonance Imaging (MRI) analysis remains challenging. Besides whole prostate gland segmentation, the capability to differentiate between the blurry boundary of the Central Gland (CG) and Peripheral Zone (PZ) can lead to differential diagnosis, since the frequency and severity of tumors differ in these regions. To tackle the prostate zonal segmentation task, we propose a novel Convolutional Neural Network (CNN), called USE-Net, which incorporates Squeeze-and-Excitation (SE) blocks into U-Net, i.e., one of the most effective CNNs in biomedical image segmentation. Especially, the SE blocks are added after every Encoder (Enc USE-Net) or Encoder-Decoder block (Enc-Dec USE-Net). This study evaluates the generalization ability of CNN-based architectures on three T2-weighted MRI datasets, each one consisting of a different number of patients and heterogeneous image characteristics, collected by different institutions. The following mixed scheme is used for training/testing: (i ) training on either each individual dataset or multiple prostate MRI datasets and (ii ) testing on all three datasets with all possible training/testing combinations. USE-Net is compared against three stateof-the-art CNN-based architectures (i.e., U-Net, pix2pix, and Mixed-Scale Dense Network), along with a semi-automatic continuous max-flow model. The results show that training on the union of the datasets generally outperforms training on each dataset separately, allowing for both intra-/crossdataset generalization. Enc USE-Net shows good overall generalization under any training condition, while Enc-Dec USE-Net remarkably outperforms the other methods when trained on all datasets. These findings reveal that the SE blocks' adaptive feature recalibration provides excellent cross-dataset generalization when testing is performed on samples of the datasets used during training. Therefore, we should consider multi-dataset training and SE

Characterisation of the physical performance of the new integrated PET/CT system Discovery ST (GE Medical Systems) has been performed following the NEMA NU 2-1994 (N-94) and the NEMA NU 2-2001 (N-01) standards in both 2D and 3D acquisition configuration. The Discovery ST combines a four or eight multi-slice helical CT scanner with a PET tomograph which consists of 10,080 BGO crystals arranged in 24 rings. The crystal dimensions are 6.3 x 6.3 x 30 mm(3) and they are organised in blocks of 6 x 6 crystals, coupled to a single photomultiplier tube with four anodes. The 24 rings of the PET system allow 47 images to be obtained, spaced by 3.27 mm, and covering an axial field of view of 157 mm. The low- and high-energy thresholds are set to 375 and 650 keV, respectively. The coincidence time window is set to 11.7 ns. Using the NEMA N-94 standard, the main results were: (1) the average (radial and tangential) transverse spatial resolution (FWHM) at 1, 10 and 20 cm off axis was 6.28 mm, 7.09 mm and 7.45 mm in 2D, and 6.68 mm, 7.72 mm and 8.13 mm in 3D; (2) the sensitivity for true events was 8,567 cps/kBq/cc in 2D and 36,649 cps/kBq/cc in 3D; (3) the scatter fraction was 15% in 2D and 30% in 3D; (4) the peak true events rate, the true events rate at 50% of the system dead-time and the true events rate when equal to the random events rate were 750 kcps at 189.81 kBq/cc, 744 kcps at 186.48 kBq/cc and 686 kcps at 150.59 kBq/cc, respectively, in 2D, and 922 kcps at 44.03 kBq/cc, 834 kcps at 53.28 kBq/cc and 921 kcps at 44.03 kBq/cc in 3D; (5) the noise equivalent count (NEC) peak rate was 270 kcps at 34.38 kBq/cc in 3D, with random coincidences estimated by delayed events. Using the NEMA N-01 standards the main results were: (1) the average transverse and axial spatial resolution (FWHM) at 1 cm and 10 cm off axis was 6.28 (4.56) mm and 6.88 (6.11) mm in 2D, and 6.29 (5.68) mm and 6.82 (6.05) mm in 3D; (2) the average sensitivity for the two radial positions (r=0 cm and r=10 cm) was 1.93 cps/kBq in 2D and 9.12 cps/kBq in 3D; (3) the scatter fraction was 19% in 2D and 45% in 3D; (4) the NEC peak rate was 54 kcps at 46.99 kBq/cc in 2D and 45.5 kcps at 10.84 kBq/cc in 3D, when random coincidences were estimated by using k=2 in the NEC formula, while the NEC peak rate was 81 kcps at 64.43 kBq/cc and 66 kcps at 14.86 kBq/cc in 2D and 3D, respectively, when random coincidences were estimated by using k=1 in the NEC formula. The new integrated PET-CT system Discovery ST has good overall performances in both 2D and 3D, with in particular a high sensitivity and a very good 3D NEC response.

Summary: Human amnesia is a clinical syndrome exhib iting the failure to recall past events and to learn new information. Its "pure" form, characterized by a selec tive impairment of long-term memory without any disor der of general intelligence or other cognitive functions, has been associated with lesions localized within Papez's circuit and some connected areas. Thus, amnesia could be due to a functional disconnection between components of this or other neural structures involved in long-term learning and retention. To test this hypothesis, we mea sured regional cerebral metabolism with 2-[18P]fluoro-2-deoxy-n-glucose ([18p]PDG) and positron emission to-

No abstract

In breast cancer (BC) care, radiotherapy is considered an efficient treatment, prescribed both for controlling localized tumors or as a therapeutic option in case of inoperable, incompletely resected or recurrent tumors. However, approximately 90% of BC-related deaths are due to the metastatic tumor progression. Then, it is strongly desirable to improve tumor radiosensitivity using molecules with synergistic action. The main aim of this study is to develop curcumin-loaded solid nanoparticles (Cur-SLN) in order to increase curcumin bioavailability and to evaluate their radiosensitizing ability in comparison to free curcumin (free-Cur), by using an in vitro approach on BC cell lines. In addition, transcriptomic and metabolomic profiles, induced by Cur-SLN treatments, highlighted networks involved in this radiosensitization ability. The non tumorigenic MCF10A and the tumorigenic MCF7 and MDA-MB-231 BC cell lines were used. Curcumin-loaded solid nanoparticles were prepared using ethanolic precipitation and the loading capacity was evaluated by UV spectrophotometer analysis. Cell survival after treatments was evaluated by clonogenic assay. Dose–response curves were generated testing three concentrations of free-Cur and Cur-SLN in combination with increasing doses of IR (2–9 Gy). IC 50 value and Dose Modifying Factor (DMF) was measured to quantify the sensitivity to curcumin and to combined treatments. A multi-“omic” approach was used to explain the Cur-SLN radiosensitizer effect by microarray and metobolomic analysis. We have shown the efficacy of the Cur-SLN formulation as radiosensitizer on three BC cell lines. The DMFs values, calculated at the isoeffect of SF = 50%, showed that the Luminal A MCF7 resulted sensitive to the combined treatments using increasing concentration of vehicled curcumin Cur-SLN (DMF: 1,78 with 10 µM Cur-SLN.) Instead, triple negative MDA-MB-231 cells were more sensitive to free-Cur, although these cells also receive a radiosensitization effect by combination with Cur-SLN (DMF: 1.38 with 10 µM Cur-SLN). The Cur-SLN radiosensitizing function, evaluated by transcriptomic and metabolomic approach, revealed anti-oxidant and anti-tumor effects. Curcumin loaded- SLN can be suggested in future preclinical and clinical studies to test its concomitant use during radiotherapy treatments with the double implications of being a radiosensitizing molecule against cancer cells, with a protective role against IR side effects.

Prostate imaging analysis is difficult in diagnosis, therapy, and staging of prostate cancer. In clinical practice, Magnetic Resonance Imaging (MRI) is increasingly used thanks to its morphologic and functional capabilities. However, manual detection and delineation of prostate gland on multispectral MRI data is currently a time-expensive and operator-dependent procedure. Efficient computer-assisted segmentation approaches are not yet able to address these issues, but rather have the potential to do so. In this paper, a novel automatic prostate MR image segmentation method based on the Fuzzy C-Means (FCM) clustering algorithm, which enables multispectral T1-weighted (T1w) and T2-weighted (T2w) MRI anatomical data processing, is proposed. This approach, using an unsupervised Machine Learning technique, helps to segment the prostate gland effectively. A total of 21 patients with suspicion of prostate cancer were enrolled in this study. Volume-based metrics, spatial overlap-based metrics and spatial distance-based metrics were used to quantitatively evaluate the accuracy of the obtained segmentation results with respect to the gold-standard boundaries delineated manually by an expert radiologist. The proposed multispectral segmentation method was compared with the same processing pipeline applied on either T2w or T1w MR images alone. The multispectral approach considerably outperforms the monoparametric ones, achieving an average Dice Similarity Coefficient 90.77 ± 1.75, with respect to 81.90 ± 6.49 and 82.55 ± 4.93 by processing T2w and T1w imaging alone, respectively. Combining T2w and T1w MR image structural information significantly enhances prostate gland segmentation by exploiting the uniform gray appearance of the prostate on T1w MRI.