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Diffusion tensor imaging (DTI) is amongst the simplest mathematical models available for diffusion magnetic resonance imaging, yet still by far the most used one. Despite the success of DTI as an imaging tool for white matter fibers, its anatomical underpinnings on a microstructural basis remain unclear. In this study, we used 65 myelin-stained sections of human premotor cortex to validate modeled fiber orientations and oft used microstructure-sensitive scalar measures of DTI on the level of individual voxels. We performed this validation on high spatial resolution diffusion MRI acquisitions investigating both white and gray matter. We found a very good agreement between DTI and myelin orientations with the majority of voxels showing angular differences less than 10°. The agreement was strongest in white matter, particularly in unidirectional fiber pathways. In gray matter, the agreement was good in the deeper layers highlighting radial fiber directions even at lower fractional anisotropy (FA) compared to white matter. This result has potentially important implications for tractography algorithms applied to high resolution diffusion MRI data if the aim is to move across the gray/white matter boundary. We found strong relationships between myelin microstructure and DTI-based microstructure-sensitive measures. High FA values were linked to high myelin density and a sharply tuned histological orientation profile. Conversely, high values of mean diffusivity (MD) were linked to bimodal or diffuse orientation distributions and low myelin density. At high spatial resolution, DTI-based measures can be highly sensitive to white and gray matter microstructure despite being relatively unspecific to concrete microarchitectural aspects.
Here, we describe a new immersion-based clearing method suitable for optical clearing of thick adult human brain samples while preserving its lipids and lipophilic labels such as 1,1′-dioctadecyl-3,3,3′,3′-tetramethylindocarbocyanine perchlorate (DiI). This clearing procedure is simple, easy to implement, and allowed for clearing of 5 mm thick human brain tissue samples within 12 days. Furthermore, we show for the first time the advantageous effect of the Periodate-Lysine-Paraformaldehyde (PLP) fixation as compared to the more commonly used 4% paraformaldehyde (PFA) on clearing performance.
Fragestellung: Die meniscofemoralen Ligamente (MFL) ant. (Humphry) und post. (Wrisberg) begleiten inkonstant das hintere Kreuzband vom lateralen Meniskus zur medialen Femurkondyle, sie üben eine Außenmeniskusstellfunktion aus. Bisherige Literatur gibt eine stark variierende Inzidenzrate der MFL an, die durch eine hohe Fallzahl von untersuchten Kniegelenken in dieser Arbeit verifiziert werden soll.
Methoden: 122 frische, unversehrte Kniegelenke von 31 männlichen und 30 weiblichen Leichen des Sektionsgutes der Rechtsmedizin Frankfurt, wurden auf Inzidenz und Morpholgie von ant. und post. MFL untersucht, ein ventraler (n.Souryal) und dorsaler Nochweitenindex (NWI) erstellt sowie eine morphologische Typisierung der vorhandenen ant. und post. MFL vorgenommen
Ergebnisse: 96% der Kniegelenke zeigten ein oder beide MFL, davon post. MFL in 82% und ant. MFL in 58%. Beide Ligamente in beiden Knien einer Leiche kamen zu 33% vor. Statistisch wurde keine Korrelation zwischen Alter, Geschlecht, Seite und der Inzidenz der Ligamente gefunden. vNWI: w 0,22; m 0,24. dorsaler NWI: w 0,26; m 0,28. Einteilung des post. MFL (Wrisberg): Typ I Strang, Typ II Fächer, Typ III kein meniskaler Ansatz.
Schlussfolgerungen: Die Inzidenz und damit auch klinische Relevanz beider MFL liegt höher als bisher angenommen, z.B. in der Beurteilung von Knie-MRTs als falschpos. Rupturen oder in der klinischen Untersuchung als Abschwächung einer hinteren Schublade. Die Typisierung zeigt einen Zusammenhang des post. MFL mit einem dritten Kreuzband auf: nach Evolution des aufrechten Ganges und damit weniger Rotation im Kniegelenk bildet es sich zurück.
This study highlights the importance of insect evidence by evaluating 949 insect-associated cases, including 139 entomological reports, from 2001 to 2019 at the Institute of Legal Medicine Frankfurt/Germany. With a high number of cases in the summer months and a low number in the colder season, 78.5% of the bodies were found indoors, regardless of year or month. In more than 80% of the cases, where PMI information was available (n = 704), the presumed PMI ranged from 1 to 21 days, a period during which entomological evidence can provide a day-specific estimate of PMImin. In cases where insects have been identified to species level (n = 279), most bodies were infested by one or two species with a maximum of 10 different species. Overall, a total of 55 insect species were found. Information on biology, activity and distribution of the most abundant taxa is given and applied for 5 case histories estimating different PMImins of up to over 6 months. Despite proved importance and scientific development of forensic entomology, insects are still rarely considered as a tool in forensic case work. The main reasons are a lack of awareness and (too) late involvement of a forensic entomologist. Our work shows that forensic entomology is an independent discipline that requires specialist expertise.
Recently, several magnetic resonance imaging contrast mechanisms have been shown to distinguish cortical substructure corresponding to selected cortical layers. Here, we investigate cortical layer and area differentiation by automatized unsupervised clustering of high-resolution diffusion MRI data. Several groups of adjacent layers could be distinguished in human primary motor and premotor cortex. We then used the signature of diffusion MRI signals along cortical depth as a criterion to detect area boundaries and find borders at which the signature changes abruptly. We validate our clustering results by histological analysis of the same tissue. These results confirm earlier studies which show that diffusion MRI can probe layer-specific intracortical fiber organization and, moreover, suggests that it contains enough information to automatically classify architecturally distinct cortical areas. We discuss the strengths and weaknesses of the automatic clustering approach and its appeal for MR-based cortical histology.
