Three-Dimensional Atlas System for Mouse and Rat Brain Imaging Data

Tomographic neuroimaging techniques allow visualization of functionally and structurally specific signals in the mouse and rat brain. The interpretation of the image data relies on accurate determination of anatomical location, which is frequently obstructed by the lack of structural information in the data sets. Positron emission tomography (PET) generally yields images with low spatial resolution and little structural contrast, and many experimental magnetic resonance imaging (MRI) paradigms give specific signal enhancements but often limited anatomical information. Side-by-side comparison of image data with conventional atlas diagram is hampered by the 2-D format of the atlases, and by the lack of an analytical environment for accumulation of data and integrative analyses. We here present a method for reconstructing 3-D atlases from digital 2-D atlas diagrams, and exemplify 3-D atlas-based analysis of PET and MRI data. The reconstruction procedure is based on two seminal mouse and brain atlases, but is applicable to any stereotaxic atlas. Currently, 30 mouse brain structures and 60 rat brain structures have been reconstructed. To exploit the 3-D atlas models, we have developed a multi-platform atlas tool (available via The Rodent Workbench, http://rbwb.org) which allows combined visualization of experimental image data within the 3-D atlas space together with 3-D viewing and user-defined slicing of selected atlas structures. The tool presented facilitates assignment of location and comparative analysis of signal location in tomographic images with low structural contrast

Hippocampal Adult Neurogenesis Is Maintained by Neil3-Dependent Repair of Oxidative DNA Lesions in Neural Progenitor Cells

SummaryAccumulation of oxidative DNA damage has been proposed as a potential cause of age-related cognitive decline. The major pathway for removal of oxidative DNA base lesions is base excision repair, which is initiated by DNA glycosylases. In mice, Neil3 is the main DNA glycosylase for repair of hydantoin lesions in single-stranded DNA of neural stem/progenitor cells, promoting neurogenesis. Adult neurogenesis is crucial for maintenance of hippocampus-dependent functions involved in behavior. Herein, behavioral studies reveal learning and memory deficits and reduced anxiety-like behavior in Neil3−/− mice. Neural stem/progenitor cells from aged Neil3−/− mice show impaired proliferative capacity and reduced DNA repair activity. Furthermore, hippocampal neurons in Neil3−/− mice display synaptic irregularities. It appears that Neil3-dependent repair of oxidative DNA damage in neural stem/progenitor cells is required for maintenance of adult neurogenesis to counteract the age-associated deterioration of cognitive performance

Geografi, demografi og høyteknologisk medisin – hvordan kan det leveres et likeverdig helsetilbud i Norge? En helsemodell basert på reisetid, befolkningsgrunnlag og medisinsk kvalitet.

Sammendrag Hva er problemet med dagens sykehusstruktur? • Pasientene ønsker best mulig helsetilbud nærmest mulig. • Mange sykehus har ikke livsgrunnlag med dagens høyteknologiske medisin og faglige anbefalinger om pasientvolum. • Dagens sykehusstruktur er inkonsistent, uhensiktsmessig og kostbar, og derfor delvis dårlig utnyttet. • Pasientene mottar ikke likeverdige helsetjenester. • Ingen vet hvor sykehusene bør være og hva de skal gjøre. • Endeløse og kostbare utredninger. • Ingen tydelige parametere for lokalisering og funksjonsfordeling følges, men den som mobiliserer mest oppmerksomhet og følelser vinner. Hva er årsaken til problemet? • Norge har utfordrende topografi og spredt befolkning. • «Sykehus» er ikke en entydig størrelse, men må sees i nivåer av kompleksitet. • Det finnes ingen tydelige parametere for å stenge/nedgradere eller oppretter/oppgradere ett sykehus. • Faglige anbefalinger følges ikke, eller feiltolkes. • Opptaksområder følger administrative grenser og ikke pasientens behov. • Helseforetak, og ikke helseregion eller Helse- og omsorgsdepartementet, finner ofte på lokale løsninger. Hva er hypotesen vår? • En metode med reisetid og befolkningsgrunnlag (Helsemodell) som parametere basert på faglig grunnlag kan bidra til å plassere sykehus og sykehusfunksjoner over hele landet. • Målet om ett likeverdig og tilfredsstillende helsetilbud er nærmere med Helsemodellen enn med dagens sykehusstruktur. • Faglig solide sykehus er bedre rustet for framtidens befolkningsutvikling. NIVÅ 1 Helsehus 2 Medisinsk sykehus 3 Akuttsykehus 4 Stort Akuttsykehus 5 Regionalt sykehus 6 Nasjonalt sykehus Maksimum reisetid 30-60 min 60 min 60 min 60 min Ingen Ingen Minimum befolkningsgrunnlag 5.000 30.000 60.000 120.000 Ingen Ingen Ideelt befolkningsgrunnlag 10.000- 150.000 60.000- 300.000 120.000- 450.000 240.000- 600.000 500.000- 1.500.000 1.000.000 Hva er resultatene? • Til tross for krav om tilstrekkelig befolkningsgrunnlag foreligger det livsgrunnlag for like mange sykehus. • Mange sykehus som med dagens opptaksområder framstår med lavt befolkningsgrunnlag har egentlig høyere befolkningsgrunnlag. • Hammerfest, Gravdal, Hadsel, Sandnessjøen og Voss har enten ikke tilstrekkelig befolkningsgrunnlag eller er feilplassert. • SI Hamar slås sammen med SI Elverum. • Nye sykehus i Alta, Steinkjer, Sandnes, Oslo Sør (Ski) og Oslo Nord (Ullensaker). • Antall sykehus med akuttkirurgi reduseres fra 45 til 31. • Antall barneavdelinger er tilnærmet uendret. Det stenges fem (Hammerfest, Levanger, Kristiansund, Førde og Arendal), mens det åpnes fire (Harstad, Steinkjer, Bærum og Oslo Sør). • For å ha bedre tilgang for hele befolkningen trengs det en betydelig oppgradering av akutte helsetjenester utenfor sykehuset (oppgraderte legevakt/helsehus og ambulanse). • I 2040 vil det kun være behov for et ekstra sykehus i Stjørdal. Noen mindre sykehus i de største byene bør oppgraderes. • Dagens sykehusstruktur vil i Stavanger og Osloområde ha kapasitetsproblemer eller skape gigant sykehus. • Ingen av de nedlagte sykehus vil i 2040 oppnå minimum befolkningsgrunnlag. Hva er årsaken til resultatene? • I dagens sykehusstruktur har alle fylker kommuner som er allokert til feil, det vil si at kommunene ikke sokner til nærmeste sykehus. • Konsistent gjennomføring av Helsemodellens metode med varierende nivåinndeling etter befolkningsgrunnlag og reisetid, og allokering av kommuner til nærmeste sykehus. Hva betyr resultatene? • Mer lik og høy kvalitet i hele spesialisthelsetjenesten. • Mer likeverdige helsetjenester til folk flest. • Raskere og rimeligere prosess i omorganisering av sykehusstruktur. • Framtidsrettet sykehusstruktur

LFP predictions from linear filters and multiple regression.

<p>(<b>A</b>) Prediction of LFP (blue) and spikes (black) using order-2 Butterworth filters at 600 Hz. The original broad-band signal is shown in red. The distortion of the spike waveform can be seen in the expanded traces in B. (<b>B</b>) Example of an extracellular spike in the original signal (red) and the corresponding high-pass (black) and low-pass (blue) filtered traces using an order-2 Butterworth filter. (<b>C</b>) Prediction of LFP and spikes using linear regression, with a sliding time-window of 100 ms. There is higher noise in the LFP estimate (blue in B) and the spike estimate (black in B) than in the original broad-band signal (red in A) and the filtered spike trace (black in A). (<b>D</b>) The same spike with original broad-band signal (red) residual from multiple regression (black) and LFP prediction from multiple regression (blue).</p

Principle for modelling and subtracting the LFP.

<p>(<b>A</b>) Rostrocaudal localization of the laminar electrode array used to record the local field potential (LFP) in the mouse hippocampus. (<b>B</b>) Localization of the laminar electrode array in the coronal plane of the mouse brain (lateromedial and dorsoventral localization). (<b>C</b>) The panel shows the time windows used to model the LFP. The dashed red line is a tricube window (here: 100 ms wide) used to get a local estimate around the spike in the center (0 ms). The dashed green line is flipped tricube windows used to remove the influence of the spike waveform from the fitted LFP model. Three spikes are edited out. The solid red line is the weight function actually used to estimate the regression model, computed as the product of the dashed red and green lines. The corresponding LFP signal is shown in blue. The dashed black line is the DC level. (<b>D</b>) Depth profile of temporally weighted LFP traces recorded from the mouse hippocampus. A microscope image of the tip of the laminar electrode array is shown in the background. The 16 light spots in the center of the probe are the recording sites. The electrode array can be seen to cover the full depth profile trough the hippocampal layers from CA1 to DG. Recording from a set of reference channels in a laminar electrode can give an estimate of the local field potential on a different channel with spikes, thus allowing the estimated LFP to be subtracted in order to recover the spike waveforms from the LFP. The recovered waveforms are expressed as the prediction errors. The illustrations of the mouse brain (<b>A</b>, <b>B</b> and <b>D</b>) are adapted from Paxinos and Franklin <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082141#pone.0082141-Paxinos1" target="_blank">[56]</a>.</p

Waveform estimation with spike-triggered average and multiple regression.

<p>(<b>A–F</b>) Estimation of a biphasic square-wave pulse phase-locked to different theta phases with spike-triggered average (A–C) and multiple regression (D–F). The red line corresponds to a dummy signal inserted into the hippocampal LFP. The blue line shows the average regression or filter estimates with standard deviations in the error bars. (<b>G–L</b>) Estimation of an extracellular spike phase-locked to different theta phases with spike-triggered average (G–I) and multiple regression (J–L). The full depth profile from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082141#pone-0082141-g001" target="_blank">Figure 1A</a> was inserted into the hippocampal LFP. Spike-triggered average produces larger standard deviation and phase-dependent systematic error.</p

Waveform estimation with high-pass filtering and multiple regression.

<p>(<b>A–C</b>) Estimation of a biphasic square-wave pulse using (A) multiple regression, (B) on-line high-pass filter, or (C) anti-causal high-pass filter. The red line corresponds to a dummy signal inserted into the hippocampal LFP. The blue line shows the average regression or filter estimates with standard deviations in the error bars. (<b>D–F</b>) Estimation of an extracellular spike with the same methods as in A–F. The full depth profile from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082141#pone-0082141-g001" target="_blank">Figure 1A</a> was inserted into the hippocampal LFP.</p

Estimating Extracellular Spike Waveforms from CA1 Pyramidal Cells with Multichannel Electrodes

<div><p>Extracellular (EC) recordings of action potentials from the intact brain are embedded in background voltage fluctuations known as the “local field potential” (LFP). In order to use EC spike recordings for studying biophysical properties of neurons, the spike waveforms must be separated from the LFP. Linear low-pass and high-pass filters are usually insufficient to separate spike waveforms from LFP, because they have overlapping frequency bands. Broad-band recordings of LFP and spikes were obtained with a 16-channel laminar electrode array (silicone probe). We developed an algorithm whereby local LFP signals from spike-containing channel were modeled using locally weighted polynomial regression analysis of adjoining channels without spikes. The modeled LFP signal was subtracted from the recording to estimate the embedded spike waveforms. We tested the method both on defined spike waveforms added to LFP recordings, and on in vivo-recorded extracellular spikes from hippocampal CA1 pyramidal cells in anaesthetized mice. We show that the algorithm can correctly extract the spike waveforms embedded in the LFP. In contrast, traditional high-pass filters failed to recover correct spike shapes, albeit produceing smaller standard errors. We found that high-pass RC or 2-pole Butterworth filters with cut-off frequencies below 12.5 Hz, are required to retrieve waveforms comparable to our method. The method was also compared to spike-triggered averages of the broad-band signal, and yielded waveforms with smaller standard errors and less distortion before and after the spike.</p></div

Depth profile and wavelet spectrum of the spike triggered hippocampal LFP.

<p>(<b>A</b>) Depth profile of the local field potential (LFP) in the mouse hippocampus, subfields CA1 and DG. The plotted signals are average LFP traces triggered on the action potentials from an isolated CA1 pyramidal cell. The LFP was recorded using a 16-site laminar electrode array, with 50 µm between the recording sites. The LFP traces are broad-band filtered between 0.5 Hz and 6 kHz. (<b>B</b>) Wavelet spectrum (Paul mother wavelet, m = 4) of the fourth LFP trace in panel A, counting from the top, <i>i.e.</i> the one with the strongest spike amplitude in the CA1 LFP. The relative signal power is color coded from red (highest) to blue (no signal). Common high-pass filtering frequencies used for extracellular multi-unit recording (300 Hz and 600 Hz) intersects the bulk of the wavelet spectrum. The waveform corresponding to the wavelet spectrum is shown in white. The Paul wavelet was chosen because its asymmetric shape and high temporal resolution and makes it well suited for analyzing spike waveforms.</p

Amplitude distribution and clustering-cutting scatter-plots.

<p>(<b>A</b>) Predicted amplitudes of EC spikes with variable amplitudes using high-pass filtering (open circles) or multiple regression (filled dots). The dashed diagonal indicates perfect estimate. The amplitude estimated from high-pass filtering is negatively biased, whereas regression produces an unbiased estimate. (<b>B</b>) Average waveforms with standard deviation from the cell isolated in A and B. It can be seen how high-pass filtering delays the waveform, depresses the amplitude, and adds a trailing “bump”. (<b>C–D</b>) Scatter-plot of the amplitudes of recorded EC spikes from CA1 cells on three channels (range 0–350 µV) on a laminar electrode using high-pass filtering (C) and multiple regression (D). The shape of the cluster of amplitudes produced by an isolated cell (red dots) is indicated with a transparent red surface. It can be seen how high-pass filtering and multiple regression produce different ranges and correlation structures for spike amplitudes of the cell.</p