Dataset for the study "Optimal inter-electrode distances for maximizing single unit yield per electrode in neural recordings"

21.15109/ARP/3FWR4WMeszéna, DomokosDomokosMeszéna0000-0003-4042-2542HUN-REN Research Centre for Natural SciencesFadel, WardWardFadelHUN-REN Research Centre for Natural SciencesTóth, RóbertRóbertTóth0000-0003-4531-3337HUN-REN Wigner Research Centre for PhysicsPaulk. Angelique C.Angeliqueelique C.Massachusetts General Hospital, Harvard Medical SchoolCash, Sydney S.Sydney S.CashMassachusetts General Hospital, Harvard Medical SchoolWilliams, ZivZivWilliamsMassachusetts General Hospital, Harvard Medical SchoolKiss, TamásTamásKissHUN-REN Wigner Research Centre for PhysicsStippinger, MarcellMarcellStippingerHUN-REN Wigner Research Centre for PhysicsWittner, LuciaLuciaWittnerHUN-REN Research Centre for Natural SciencesFiáth, RichárdRichárdFiáth0000-0001-8732-2691HUN-REN Research Centre for Natural SciencesSomogyvári, ZoltánZoltánSomogyvári0000-0002-4385-3025HUN-REN Wigner Research Centre for PhysicsDataset for the study "Optimal inter-electrode distances for maximizing single unit yield per electrode in neural recordings"ARP2025Medicine, Health and Life Sciencesspike sortinghigh-density electrophysiological recordingmultielectrode arrayneocortexthalamussingle unitFiáth, RichárdRichárdFiáthHUN-REN Research Centre for Natural Sciences2024-07-232025-11-10extracellular electrophysiological recordings from the neocortex and thalamus of rodents10.1016/j.jneumeth.2018.08.02010.1088/1741-2552/ac0f4942738425065082657297870023291284581539616105423243276925369574480000470320832811397042285142609815929084469063492701101424255274694901609886156946617610370483532877815436679372700664301588458204817959120374549076660559485947907313298211273238062296338755682315990883320664413568632150169866251534741080698091660379989585825160785500152013289870410984045371002627629808262257575261184301260397880263208922261509172263716450262030610263454589262045080263083663532095924532258248533405086532396398533502999675673992668960223675922510668597314900509064900939412900723585139560246813832505543161803613316455240315380715314522925316616330315188056316738593315348963316603911315077954316576915642036993641421367641777333641915851641974195813144529806946215812706572806813091108616814510854460381085866014165528146916488728004823176881602421715956204590712124084212456748066348838450234590018404221257934194504536927864123687416184687110299386121124417512883761735427125492162391661414553297370631891238084855250580154234124525624186209686824094929838623689171234411548276910486079622949557184137122167985324626389444551717907458479063456264214956394137290829871059152093150112705715011316565382291206862230263489050136758704945840292625082971606214579989867703096924516171257982568123238317025433700642505055197713573830187629092014052535372178121713036071551914389285021412139text/csvtext/csvtext/csvapplication/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5text/x-pythonapplication/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5application/x-hdf5text/csvtext/csvtext/csvtext/csv1.0CC BY 4.0Abstract of the study "Optimal inter-electrode distances for maximizing single unit yield per electrode in neural recordings": State-of-the-art high-density multielectrode arrays enable the recording of simultaneous spiking activity from hundreds of neurons. Although significant efforts have been dedicated to enhancing neural recording devices and developing more efficient sorting algorithms, there has been relatively less focus on the allocation of microelectrodes—a factor that undeniably affects spike sorting effectiveness and ultimately the total number of detected neurons. Here, we systematically examined the relationship between optimal electrode spacing and spike sorting efficiency by creating virtual sparser layouts from high-density recordings through spatial downsampling. We assessed spike sorting performance by comparing the quantity of well-isolated single units per electrode in sparse configurations across various brain regions (neocortex and thalamus), species (rat, mouse, and human) and various spike-sorting algorithms. Enabling the theoretical estimation of optimal electrode arrangements, we complement experimental results with a geometrical modeling framework. Contrary to the general assumption that higher electrode density inherently leads to more efficient sorting, both our theoretical and experimental results reveal a clear optimum for electrode spacing specific to species and regions. We demonstrate that carefully choosing optimal electrode distances could yield a total of 1.7 to 3.75 times increase in spike sorting efficiency. These findings emphasize the necessity of speciesand region-specific microelectrode design optimization.Kilosort, 2.0MATLAB, 2019bPhy, 2Spikeinterface, 0.100<h3>Dataset structure</h3> Details of collection, processing and analysis of recordings are described in the published article. Each recording and corresponding metadata, single unit properties and quality metrics were packaged in the <a href="https://www.nwb.org/">Neurodata Without Borders: Neurophysiology version 2.0 (NWB:N 2.0) data format</a> using the <a href="https://neuroconv.readthedocs.io/en/main/">NeuroConv Python package</a>. A single NWB file was created from each recording. NWB files were placed in folders based on the brain area (Neocortex or Thalamus), the animal model (Rat, Mouse or Human) and the channel number (e.g., 256, 128, 64, 32 or 16). The filename of the NWB file (identifier) was constructed by concatenating the above information and the identifier of the animal (e.g., Rat01_Neocortex_256channel.nwb). In the case of multiple probe insertions being performed in a single animal, or recordings were carried out at multiple brain depths, this information was also incorporated in the NWB filename (e.g., Rat01_Thalamus_256ch_Insertion1.nwb). The original human Neuropixels recordings can be found in the <a href="https://datadryad.org/stash/dataset/doi:10.5061/dryad.d2547d840">Dryad repository</a>. The CSV file named "Animal_characteristics_" contains information about the subjects (e.g., age, weight, sex). The "Recording_characteristics" CSV file lists several useful properties for each NWB file including the file size, the duration of the recording, the cortical or thalamic location and stereotaxic coordinates, the single unit yield or the average signal-to-noise ratio of single units. <h3>NWB file structure</h3> Each NWB file contains several main groups which are similar to directories. The acquisition group contains the continuous wideband multichannel data (‘ElectricalSeriesRaw’) in a compressed form, as well as several parameters related to the raw data such as the measurement unit or the data conversion number. The general group contains metadata about the experiments and consists of several subgroups, related to the recording probe (‘general/devices’; ‘general/extracellular_ephys’) or the subjects of the experiments (‘general/subject’). Former subgroups carry information about the probe location (brain area and stereotaxic coordinates), while the latter contains metadata about the animal (e.g., sex, species, subject ID, or weight). Information about spike sorting and single units and corresponding data are available in the units group. For each unit, we included here the mean and standard deviation of their spike waveform on all channels, calculated from the wideband data (‘waveform_mean’; ’waveform_sd’). Several single unit properties (e.g., half_width, unit_name, peak_channel) and cluster quality metrics (e.g., isolation_distance, amplitude_cutoff, presence ratio), as well as the spike times and spike count of each unit were saved in the units group.The structure of NWB files can be explored using the <a href="https://www.hdfgroup.org/downloads/hdfview/">HDFView</a> software. <h3>Interacting with the NWB files</h3> After downloading, the structure of NWB files can be explored using the freely available <a href="https://www.hdfgroup.org/downloads/hdfview/">HDFView</a> software. Users can import data from NWB files offline using the <a href="https://github.com/NeurodataWithoutBorders/pynwb/">PyNWB</a> and <a href="https://github.com/NeurodataWithoutBorders/matnwb/">MatNWB</a> APIs, or using <a href="https://github.com/SpikeInterface">SpikeInterface</a>. Loaded samples of the raw data have to be multiplied by a conversion number (e.g., 0.195 for multichannel recordings) to get the amplitudes in microvolts. Here we provide some examples how users can import data from NWB files using the MATLAB-based MatNWB API. Loading a short segment (20.000 samples corresponding to 1 second of data) of the raw wideband recording on all channels (e.g., 128 in the example): <code> 1. nwb = nwbRead('Rat01_Neocortex_128channel.nwb'); </code> <code> 2. dataChunk=nwb.acquisition.get('ElectricalSeriesRaw').data.load([1, 1], [128, 20000]); </code> It is important to note that TimeSeries data types in NWB files are stored with time in the first dimension and channels in the second, but dimensions are reversed in MatNWB. Loading and plotting the mean spike waveform of a specific single unit on the peak waveform channel: <code> 3. peakChannels = nwb.units.vectordata.get('peak_waveform_channel').data.load(); </code> <code> 4. meanWaveforms = nwb.units.vectordata.get('mean_waveform_all_channels_filt').data.load(); </code> <code> 5. mySingleUnit = 11; </code> <code> 6. singleUnitWaveform = meanWaveforms(peakChannels(mySingleUnit), :, mySingleUnit); </code> <code> 7. plot(singleUnitWaveform); </code> Loading the isolation distance quality metric of all units found in a single NWB file: <code> 8. IDvalues = nwb.units.vectordata.get('isolation_distance').data.load(); </code> Spike times are stored in a special structure called ragged arrays consisting of two vectors. The spike_times vector contains all spike times of all single units concatenated one after the other, while the spike_times_index vector stores where the spike times of individual single units are located in the spike_index vector (see also <a href="https://neurodatawithoutborders.github.io/matnwb/tutorials/html/ecephys.html#H_97F533F8">https://neurodatawithoutborders.github.io/matnwb/tutorials/html/ecephys.html#H_97F533F8</a>). We can load the spikes times of a specific single unit (in seconds) the following way: <code> 9. allSpikeTimes = nwb.units.spike_times.data.load(); </code> <code> 10. spikeTimesIndex = nwb.units.spike_times_index.data.load(); </code> <code> 11. spikesOfSingleUnit2 = allSpikeTimes(spikeTimesIndex(1)+1 : spikeTimesIndex(2)); </code> SpikeInterface can also be used to load the wideband data and single unit properties (in Python, works only with version 0.13): <code> 1. import spikeextractors as se </code> <code> 2. nwbPath = 'Rat01_Neocortex_128channel' </code> <code> 3. recording = se.NwbRecordingExtractor(nwbPath) </code> <code> 4. sorting = se.NwbSortingExtractor(nwbPath) </code> <code> 5. mySingleUnit = 2 </code> <code> 6. sorting.get_unit_property(mySingleUnit,'isolation_distance') </code> We also provide a Python script ("NWB_tutorial_script.py") which can be used to load, visualize and preprocess (e.g., filter) files in the dataset using pyNWB.Hungarian Brain Research ProgramNAP2022-I-2/2022Hungarian Academy of SciencesBolyai János ScholarshipNational Research, Development, and Innovation FundSTARTING_24 (150574)National Research, Development, and Innovation Fund2019-2.1.7-ERA-NET-2021-00023Hungarian Research Network (HUN-REN)SA-114/2021Hungarian Research Network (HUN-REN)TECH-2024-20New National Excellence Program of the Ministry for Culture and InnovationÚNKP-23-5-PPKE-128Hungarian National Research, Development, and Innovation OfficeK135837Hungarian National Research, Development, and Innovation OfficePD143582Hungarian Research NetworkARP Ambassador Program