I am generating a final connectome matrix using an atlas with 268 nodes. I checked the image and it progresses from 1 to 268, so i skipped the label convert step.

i issued the tck2connectome command and the connectome was completed without any errors but when i examined the final csv file,the weights looked really high, i have attached a screenshot.
For the weights,i used tcksift2.

Is this expected? does this indicate something is wrong?

If anything, for a connectome generated using SIFT2 weights, the values in your connectome matrix look small to me. An edge to which only a single streamline was assigned would typically have a value of around 1.0. So Iâ€™m guessing that youâ€™ve included some form of additional scaling factor on top of this when determining the per-edge connectivity values.

Given the highlight in your screenshot, is your concern not that â€śthe weightsâ€ť (i.e. all connectivity values) are large, but that there are individual edges for which the connectivity values are considerably higher than others? Because if so, thatâ€™s entirely expected. Indeed having every GM area connected to every other GM area with equal density would be exceptionally unusual. The maximal value youâ€™ve highlighted is 2-3 orders of magnitude higher than the smallest non-zero value. In quantitative post mortem tract tracing studies it has been suggested that this figure is even greater; 5-6 orders of magnitude from the source Iâ€™ve used previously. In my own data Iâ€™ve seen ranges of this order (the amount of connection density variance possible between maxima and non-zero minima will depend on the number of streamlines generated). This is starkly different to e/g/ functional connectivity-based connectomes, where a measure like Pearson correlation is by definition restricted to a finite range ([-1 â€“ +1] at most).

So your connectivity matrix both being sparse (many zero or near-zero connections, fewer high-density connections) and having large fluctuations in connection densities are entirely expected for this connectivity metric.