If you attempt a seed based c= onnectivity analysis on previously released group average resting state HCP= data, you may see one or more rings of alternating high and low values&nbs= p;surrounding the seed location, akin to the ripples after dro= pping an object into a lake. In an earlier posting on this website, w= e proposed that this =E2=80=98 =E2=80=98mound and moat=E2=80=99, = or =E2=80=98ripple=E2=80=99 effect was attributable to the use of a&nb= sp;=E2=80=98sinc=E2=80=99 function during the image reconstruction process.=

Recently, we discovered that the bulk of the ripple effect is= instead attributable to a later stage of how we originally generated the d= ense connectomes.

We just released an improved group = average dense connectome for the R468 group of the 500 Subjects release= (requires ConnectomeDB login), in which the ripple effect is greatly reduc= ed. A more extensive description of the phenomenon and how it ha= s been addressed is below and on http://www.humanconnectome.org/documentation/mound-and-moat-effect.= html

Jesper Andersson, Matt Glasser, Christian Beckmann, David Van Essen, Ste=
ve Smith

April 2015

Seed-based connectivity analysis of the resting state HCP data (e.g., fr= om previously released but subsequently withdrawn group-averaged dense conn= ectome datasets) may show rings (or 'ripples') of lower (sometimes even neg= ative) correlation encircling the seed voxel, as in the illustration below.= This pattern looks counter-intuitive and neurobiologically implausible, an= d it therefore warrants explanation.

The analysis below provides evidence that multiple independent factors c= ontribute to the observed ripple effect.

**Spatially-dependent unstructured noise.**Unstructured f= MRI noise may have spatial dependencies, arising from the nature of the ima= ge acquisition, reconstruction and the interpolation methods used in motion= /distortion correction and atlas registration.**Abrupt PCA truncation.**Abrupt truncation of PCA compon= ents used for generation of a dense connectome can contribute to extensive = rippling in regions of low SNR.

Of these, the second is the most significant for group average analysis = from large numbers of subjects, including the recent HCP 500-subject releas= e. The ripple effect from this cause can be essentially eliminated by apply= ing an appropriate PCA-eigenvector weighting (rolloff) function, as describ= ed below. This rolloff strategy has the added benefit of improving SNR rela= tive to the previously released dense connectome. The improved dense connec= tome (file name HCP_S500_R468_rfMRI_MIGP_d4500ROW_zcorr.dconn.nii) is now a= vailable via ConnectomeDB:

**https://db.humanconnectome.org/data/projects/HCP_500**

*Fig.1. Top row: Ripple/Ring-of-Fire effects for a subc=
ortical seed (panel A, thalamus) and a cortical seed (panel B, cingulate co=
rtex) using abrupt truncation of 4500 PCA components. Bottom row. No ripple=
effect in either region after using a rolloff function for the PCA-eigenve=
ctor weighting (see text).*

When performing a seed-based analysis we extract a time-series from a 'g= rayordinate' (voxel or surface vertex) and then calculate the correlation b= etween that and the time-series from every other grayordinate. When a regio= n is positively correlated with the seed this provides evidence that it mig= ht be functionally connected to the seed.

However, the observed signal in a voxel (or surface vertex) includes a m= ixture of biologically relevant signal, structured noise (e.g., scanner and= physiological artifacts) and 'unstructured' noise (e.g., MRI thermal white= noise). We are only interested in correlations pertaining to the neuronal = signal and go to great lengths to reduce the structured noise through pre-p= rocessing of the data. Pure 'white' noise, because it is (spatially) white,= is not a problem except for reducing the 'SNR' of the final correlation ma= p.

**II.1. Unstructured MRI noise may not be perfectly 'white'**

If noise is truly white, it should be independent across voxels. This me= ans that if we take a large number of pairs of neighbouring voxels, calcula= te the correlation between the voxels in each pair, and average those corre= lations, the mean should be zero. With a relatively simple neuroimaging pro= tocol, e.g., data acquired using a gradient echo sequence and no parallel i= maging, the noise should approach this property of being uncorrelated.

However, modern EPI sequences often include features such as 'ramp sampl= ing,' partial k-space sampling, k-space windowing, in-plane acceleration (f= or example GRAPPA) and multi-band acquisition. All of these refinements bre= ak the 'one-measured-value-one-voxel' property of traditional sequences tha= t ensured uncorrelated noise. They will of course bring many other good thi= ngs such as the ability to acquire more data in a given time, reduced disto= rtions etc, but at the cost of introducing dependencies between voxels in a= n image.

In the reconstruction process the conflict between differences in the nu= mber of measured values to image voxels is resolved using filters that comb= ine multiple measured values. These filters are often "sinc" functions, i.e= . they combine the values using a particular mixture of positive and negati= ve weights. This leads to a particular type of dependencies between neighbo= uring voxels, such that a given voxel tends to be positively correlated wit= h its immediate neighbour, but has a negative correlation to the next voxel= away.

**II.2. Dependencies introduced by spatial pre-processing**=

As described above, reconstructed images that come off the scanner will = generally have spatially coloured (non-white) noise =E2=80=93 generally mea= ning there will be some degree of spatial dependency. Moreover, some of the= pre-processing that is performed on the data can introduce additional depe= ndencies between voxels.

The images directly from the scanner often suffer from distortions plus = artifacts from head movements, and their resolution is limited to ~1.5-2mm.= One important role of the pre-processing is to correct for distortions and= head movements and possibly to resample the data into standard space. This= necessarily involves interpolation of the data, i.e., (similarly to the re= construction described above) new voxel values are calculated through weigh= ted averages of the original voxels. These weights are obtained using the s= ame sinc function as described above. The sinc is the function that yields = the highest fidelity of the interpolated data, hence its ubiquity (though i= n HCP preprocessing we generally use a single 'spline' interpolation that e= nds up having very similar characteristics to sinc interpolation).

The result of this is inevitably additional dependencies in the data. A = given voxel will be positively correlated with its nearest neighbours, but = will be negatively correlated to its neighbours further away. Just how many= voxels away will depend on the details of both the reconstruction process = and of the relative voxel sizes of the raw images and the interpolated imag= es.

**II.3. What do we see with seed based correlation - revisited**

This dependence between neighbouring voxels has important implications f= or what we see with seed-based analysis. The assumption that the noise does= n't affect the analysis (because it is uncorrelated) is clearly an oversimp= lification. This means that if we put a seed in one voxel we would find tha= t the neighbouring voxels are positively correlated, just by virtue of the = noise in the seed voxel having spilt over into the neighbours by the interp= olation/filtering. In addition, at a distance a few voxels away from the se= ed there may be a negative correlation in all directions (i.e., a spherical= shell of negative correlations). This contribution is particularly relevan= t to subcortical domains.

In order to carry out group-level analyses such as group-averaged dense = connectome estimation and group-ICA, the HCP has first applied a group-PCA = (using a method known as MIGP =E2=80=93 MELODIC's Incremental Group-PCA [Sm= ith, NeuroImage 2014]). PCA represents a spaceXtime matrix as a series of P= CA components, each comprising a single spatial map plus a single timeserie= s. The components are sorted in order of decreasing 'importance' (variance = of the data explained). The data can then be 'reconstructed' by multiplying= each map with its associated timecourse, and adding up over components; th= e more components included, the more accurately the data is reconstructed.<= /p>

To create a 'group-average' PCA decomposition of all HCP subjects' rfMRI= datasets, one can temporally concatenate all datasets, and then apply stan= dard PCA to this (humongous) data matrix (e.g., 91282 grayordinates X 4500 = timepoints X 600 subjects X 4 bytes =3D 1TB). In practice, this can be comp= utationally infeasible (due to the large data sizes and numbers of subjects= ). It is computationally much more tractable to use the MIGP method, which = approximates this (with almost perfect accuracy) via some simple mathematic= al tricks, producing (currently) the top 4500 group-PCA components' spatial= maps.

One use of the group-PCA maps is to feed them directly into standard spa= tial ICA, to give group-ICA estimation with no further computational challe= nges. A second application is to compute a group-average dense connectome d= irectly via the PCA spatial maps =E2=80=93 again with no challenges of comp= utational resources. This will be accurate as long as the number of compone= nts estimated is 'sufficiently' large. It will generally be the case that t= he early components reflect 'important' components describing highly struct= ured signals such as biologically relevant resting-state networks (RSNs) an= d any gross artefacts, whereas later components tend to represent mainly st= ochastic (random, unstructured) noise. Hence, apart from the computational = advantages of estimating the dense connectome via an efficient group-PCA, t= he signal-to-noise ratio (SNR) of the estimated dense connectome can be gre= atly improved by estimating this via just the top few (or hundreds) of comp= onents (because much of the unstructured noise is then discarded). This ope= ration results in 'PCA filtered' data having improved SNR.

However, simple PCA filtering using equally weighted components can cont= ribute very strongly to the ring/ripple effects that occur using seed-based= correlation (in fact in many scenarios this can be by far the largest cont= ribution =E2=80=93 more than the above explanations). The following explana= tion assumes some understanding of both PCA and Fourier analysis.

**III.1. How PCA-filtering introduces ringing/rippling**

Both a PCA and a Fourier-transform can be regarded as factorising the da= ta in a similar way: Y =3D S T, where S is a set of spatial maps and T a se= t of timeseries. In the case of a (spatially-based) Fourier transform, the = maps in S will be pre-determined and sorted by spatial frequency content. I= n the case of a PCA both the maps and the time series will be estimated, an= d they are sorted in terms of decreasing ability to explain the variance in= the data.

We start by considering a hypothetical example based on data with noise = that is white both temporally and spatially. If one performs a Fourier anal= ysis one would see the (predetermined) spatial maps and time-series of Four= ier coefficients with roughly equal values for all frames and components/ma= ps. If instead one performed a PCA, each map would look roughly the same, a= nd they would all look like white noise.

However, if the noise in the data isn't white, for example if there has = been some spatial smoothing (e.g. from the image reconstruction and preproc= essing as described above), things are quite different. Because of the spat= ial smoothing, the low spatial frequencies in the data will have higher pow= er than the high spatial frequencies. Since the PCA sorts components by dec= reasing importance/power, the components will therefore be sorted by increa= sing spatial frequencies, i.e., the early components will have lower spatia= l frequencies than the later components. This does not mean that the compon= ents' spatial maps will look like a Fourier basis set; they will look like = 'random patterns of blobs,' but for the early components those blobs will b= e spatially smooth and extended, whereas for the late components a 'blob' w= ill be a single pixel.

This all means that a signal in space and time that has been factorised = by PCA can be 'reconstructed' by adding up all the components, with the smo= oth/low spatial frequency components appearing early, and the higher spatia= l frequencies later. The PCA-filtering, however, is equivalent to adding up= only the early components (though maybe several hundreds of them), i.e. us= ing only the smoothest components. This has the desirable effect of improvi= ng the SNR because the later maps are more dominated by noise than the earl= y ones, as explained above.

*However*, this also means that a signal that may potentially con=
tain valid high spatial frequencies gets reconstructed using only low-to-me=
dium frequency components. A close analogy here is trying to reconstruct a =
square wave with a truncated Fourier-series. This results in 'Gibbs-ringing=
,' i.e. a sinc-like pattern of ringing.

If we again consider the case where there is only white noise, a voxel s= hould only be correlated with itself, and the expected correlation with any= other voxel should be zero. This corresponds to a Dirac peak centred on th= e seed-voxel, i.e. it has a very high spatial frequency content. When data = has some spatial smoothness (remember that the sorting of the PCA component= s only occurs if the noise is spatially smooth), a voxel is expected to hav= e a positive correlation to neighbouring voxels, which means that the Dirac= peak turns into a 'somewhat smooth Point Spread Function (PSF),' probably = similar to a quite narrow Gaussian.

A Gaussian has fewer high frequencies than a Dirac peak, but it will nev= ertheless require a certain number of Fourier components to accurately repr= esent it, and if truncated too early, one will see ringing. Because of the = PCA-Fourier analogy described above the same holds true for the PCA-compone= nts, i.e., if they are truncated 'too early' this will lead to ringing when= reconstructing the data.

**III.2. Impact on HCP Dense Connectomes**

As part of the HCP processing the data gets preprocessed (e.g., to remov= e distortions) and resampled onto the cortical surface (all of which introd= uces some spatial smoothness) followed (at the group-level) by PCA-filterin= g of the time-series from brain grayordinates. The data (or the dense conne= ctome) is then 'reconstructed' back onto the surface using a truncated set = of PCA-components. However, it may not have all the spatial components need= ed to 'reconstruct' the true PSF of noise correlation, resulting instead in= ringing as explained above.

Unsurprisingly, this ringing can be avoided by reconstructing the signal=
with enough components to sufficiently represent the true PSF. *But that also means that one gets less of the desirable noise reduction from =
the PCA-filtering, as well as then hitting computational resource problems.=
*

Paradoxically, one can also avoid the ringing by reconstructing the sign=
al from 'sufficiently few' components, i.e., fewer components than what lea=
ds to the ringing. In order to understand that, one needs to remember that =
the data is not just noise, but consists of true signal from distributed ne=
tworks with non-trivial spatial extents. The true signal is likely to be re=
asonably represented by a limited number of PCA components, as there are on=
ly a finite number of 'true networks' accessible using the HCP resting-stat=
e fMRI protocols. The noise on the other hand will need a larger number of =
components. The observed seed-based correlation will be a superposition of =
the 'true network correlations' and the noise correlations. If the noise co=
rrelations are adequately represented (i.e., if we use 'enough components')=
it will just add to the correlations immediately adjacent to the seed and =
would not be noticed. If, on the other hand, we reconstruct the signal from=
'sufficiently few' components the noise will be so suppressed that 'only' =
signal remains and the noise PSF will have too small magnitude to be notice=
d. *But* using so few components means that there is a risk that the=
'true network correlations' aren't adequately represented.

These characteristics were evident in the previously released dense conn= ectomes by putting seeds on the lateral cortical surface, the medial cortic= al surface and the temporal pole. These areas respectively represent high, = medium and low SNR, and were associated with little, medium and severe ring= ing as the 'noise correlation' became a bigger part of the total observed c= orrelation.

**III.3. Is less more: should one use "enough components" or "suff=
iciently few"?**

The dense connectomes originally released by the HCP had been PCA-filter= ed in such a way that neither 'enough' nor 'sufficiently few' components ha= d been retained (4500 components were used, with a sharp cutoff at 4500). T= his led to ringing patterns that became worse as more subjects were include= d in the group-PCA (461 subjects in the June, 2014 release vs. 196 subjects= in the 2013 release). Given these observations, the question became: how s= hould this problem best be handled? There is no single unambiguous answer t= o that, but we describe below a solution that we consider justified on both= theoretical and empirical grounds.

**III.4. Wishart-adjusted eigenspectrum**

The 'strength' of a PCA component is referred to as its eigenvalue (stri= ctly speaking, the eigenvalue is the square of the singular-value Si associ= ated with a component i in a decomposition Y=3DUSV'). A plot of eigenvalues= as a function of component number is a monotonically decreasing curve (bec= ause by definition the PCA sorts the components that way); this curve is th= e 'eigenspectrum' =E2=80=93 and it is (by definition) monotonically decreas= ing whether the data is pure signal or pure noise (or a combination of both= , as occurs in reality). In the case of running PCA on data containing Gaus= sian white noise, the empirical distribution function of the PCA eigenvalue= s is known and follows the 'Wishart eigenvalue distribution.' In the case o= f spatially smoothed noise, the result still holds.

The eigenspectrum that we estimate from the group-PCA is in effect a sum= mation of the noise eigenspectrum (Wishart) and the signal eigenspectrum. I= f we assume that the lower values (the tail of the distribution) are domina= ted by the smoothed noise, we can fit a Wishart to this (it has some free p= arameters that can be optimised to give a good fit), and subtract this from= the empirical function. This has the effect of 'rolling off' the later PCA= components' strength (as their eigenvalues have been reduced), and the eig= enspectrum falls to zero before the highest components (4500) are reached. = This means that we no longer have a sharp cutoff in the PCA-based data/conn= ectome reconstruction, avoiding the ringing effects from the PCA. It also i= ndicates that the SNR of the reconstruction is =E2=80=9Coptimal=E2=80=9D, i= nsofar as we have done our best to limit the effects of the noise in a grad= ual way. Notably, this approach improves the SNR of the data without requir= ing additional spatial or temporal smoothing, as is often done in fMRI. In = this way, strong neurobiologically plausible signals are not blurred (maint= aining even their relatively fine details), whereas weak near-Gaussian sign= als and the effects of smoothed white noise are downweighted.

**III.5. Interaction with variance normalisation**

For the group-PCA (and in general in our applications of ICA to HCP data= ) we apply voxelwise (or grayordinate-wise) temporal variance normalisation= [Beckmann IEEE TMI 2004]. This is more complicated than just normalising t= he variance of each voxel's (or grayordinate's) timeseries to unity, as we = try to estimate what part of the data variance is structured signal, and wh= at part is unstructured noise, and only use the latter for the normalisatio= n. This is done in order to ensure a homogenous probability of false-positi= ves, i.e., in order that everywhere in the brain the chance of detecting a = false positive is the as similar as possible. In general it seems advisable= (both empirically, improving the detectability of neurobiologically reason= able connectivity patterns in regions of poor SNR, e.g., subcortically), an= d in theory (evening out the chance of false positives across the brain) to= apply this for each individual 15-minute timeseries data at the point it i= s fed into MIGP.

After applying first level variance normalization to each rfMRI run, aft= er dimensionality reduction using MIGP, the unstructured noise is no longer= homogeneous across the brain for several reasons. In general, the unstruct= ured noise is higher for surface vertices that get information from fewer (= original data) voxels and lower in surface vertices that get information fr= om more voxels (represented by the vertex areas). The vertex areas are homo= geneous on the surface of a sphere, but when the spherical mesh is deformed= to the anatomical surface, they are no longer homogeneous. Additionally, s= ubcortical voxels tend to have different unstructured noise than surface ve= rtices, and subcortical regions with higher iron content (such as the globu= s pallidus), which reduces SNR (less signal at a given TE because of a shor= ter T2*), have higher unstructured noise variance than nearby regions. Thus= , it is desirable to remove these spatial variations in unstructured noise = variance using a second variance normalization step followed by PCA without= dimensionality reduction, so that the only variance differences in the dat= a are related to structured signals.

We now give an overview of the overall process used to generate dense co= nnectomes from HCP data:

- Apply
**'first-level' variance normalisation**to each ind= ividual (15-minute) rfMRI dataset. This can either be done on the basis of = the standard MELODIC approach (of finding the variance after excluding the = strongest 30 PCA components), or, possibly more accurately, using the outpu= t of the ICA (e.g., the ICA used for FIX data denoising), one can identify = the residuals from the MELODIC PCA dimensionality estimation, and normalise= using those. - Feed all datasets into
**MIGP group-PCA**in order to esti= mate the top 4500 PCA components (spatial maps and associated eigenvalues; = the timeseries are discarded). - Fit a Wishart ('pure noise') eigenspectrum to the tail of the estimated=
eigenspectrum and
**adjust the estimated eigenspectrum by subtractin= g the noise part**. This process is referred to as ROW ('roll-off Wi= shart'). - When estimated via the ROW-adjusted PCA spatial maps, the ring of fire =
(ripple effect) is greatly reduced in the dense connectome, but some other =
artefacts remain, related to residual inhomogeneity of unstructured noise v=
ariance after MIGP dimensionality reduction. We can ameliorate these by car=
rying out a
**'second-level' variance normalisation**. We esti= mate the grayordinate-wise variance (across components) of the original MIG= P output, subtract the variance of the ROW-adjusted MIGP, resulting in a sp= atially varying map of the residual unstructured noise variance, and use (t= he square root of) that to grayordinate-wise rescale (variance normalise) t= he original MIGP maps. We then repeat the PCA, without any further dimensio= nality reduction. This procedure improves group-ICA estimation empirically = (giving a better balance between cortical and subcortical components), in a= ddition to improving dense connectomes. - We now re-fit the Wishart to the second-level variance-normalised PCA m=
aps, and again
**adjust the estimated eigenspectrum by subtracting th= e noise part**. - Finally we can
**estimate the dense connectome**by taking= the outer product of the PCA maps (weighted by the adjusted eigenspectrum)= with themselves. This initially is a grayordinatesXgrayordinates temporal = covariance matrix, which we then convert into a correlation matrix, and the= n Fisher-transform to give a z-statistic version of the correlation matrix.=

As far as we can tell this overall strategy is working well. The latest = dense connectome (grayordinate-wise seed-based functional correlation maps)= has much higher SNR than when computed from the full 'raw' data, and has n= o evidence of rings-of-fire (see HCP_S500_R468_rfMRI_MIGP_d4500ROW_zcorr.dc= onn.nii, available at ConnectomeDB:

**https://db.humanconnectome.org/data/projects/HCP_500**

We are grateful to Tim Laumann (who was particularly concerned by the in= creased severity of the RoF effect when the number of subjects increased) f= or pushing us to better understand the multiple factors contributing to the= RoF.

- Smith SM, Hyv=C3=A4rinen A, Varoquaux G, Miller KL, Beckmann CF (2014).= Group-PCA for very large fMRI datasets. N= euroimage. 101:738-49.
- Beckmann CF, Smith SM (2004) Probabilistic indepe= ndent component analysis for functional magnetic resonance imaging. IEE= E Trans Med Imaging. 23:137-52